Sudan_catch_analysis

Analysis of Sudan survey data 2012 - 2013

This R-markdown document contains all r-code used to carry out the analysis for the paper on fish catch rates and biodiversity along the Red Sea coast of Sudan. The analysis is based on three surveys: November 2012, May 2013 and November 2013.

Here the analysis is structured around the 7 management regions of the Sudanese Red Sea coast as follows:

• Present the region: Bathymetric map
• Sampling locations
• Table of effort (diffent gear types per management areas.)
• Table of species / families caught
• Depth distribution of traps by survey and area
• Catch CPUE by area and survey
• Catch CPUE distribution by gear, by species groups (should perhaps do the same for trophic groups)
• PCA analysis of species and trophic groups in catches
• CPUE by trophic group pr area and survey
• CPUE by depth and tropich group and survey
• Statistical analysis
  • test of normality shows that we need to use non-parametric and zero-adjusted approaches.
  • CPUE as a function of area, depth, survey, gear (Zero-adjusted model)
• Biodiversity analysis
  • Species accumulation curves
  • Functional diversity (Rao´s Q)

All code and data are stored on GitHub

Libraries, data etc.

Libraries, dependencies and functions

Load and manipulate data

Reading catch, station and traits data.

Neither station data, nor catch data has a complete depth record, but by combining depth para from each we can get a complete depth parameter for all stations.

Sudan map and management areas

Loads the map data for Sudan, loads the management areas from shape-file etc.

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/eriko/GitHub/Sudan2019/Sudan-master/sudan_management_areas", layer: "sudan_regions"
## with 7 features
## It has 3 fields
## Integer64 fields read as strings:  id

Allocating catch positions to management areas

Adds ‘Area’ to each line in the catch table.

Modifying dataset and creating one dataset for CPUE analysis and another for traits

Of the catch data, 12 fish registrations lack weight, (i.e. this was forgotten entered into the database during the survey). These registrations, would if included lead to 12 more registrations of CPUE = 0.

Adding traits to ‘catch’ data set (without 0-catch stations)

Some more data wrangling that adds the traits from the traits table to the catch data, using only the station with catches (there are no traits for ‘NOCATCH’ species).

Also, only select traps, gillnets and handlines as these were the only gear with sufficient numbers and consistent use to be analyzed.

Lastly adds number of gear deployed at each station to each line.

Bathymetric map of Sudan with ggplot and marmap

A nice, bathymetric map of Sudan with management areas overlaid. (Fig. 1 in MS)

March 2021: New code for making bathymetric map to include depth legend, north arrow and scale bar.

## quartz_off_screen 
##                 2

Stations plotted on maps, pr survey

Faceted map plotting position of all catch stations for each of the three surveys.

Station information

Station table

Table describing the sampling effort in each area pr survey, number of different gear types, max, min and average depth, number of traps with / without catches.

survey	id	Ntraps	Nhl	NGn	TBhrs	HLhrs	GNhrs	DepthAvg	DepthSD	DepthMax	DepthMin
2012901	1	22	0	0	694.066	0.000	0.000	42.45455	27.104256	142	13
2012901	2	54	3	3	721.549	78.000	62.000	40.89744	16.597075	71	0
2012901	3	26	0	1	451.183	0.000	14.000	30.80769	19.237503	95	8
2012901	4	5	0	0	77.084	0.000	0.000	22.60000	17.910891	54	10
2012901	5	31	0	4	677.896	0.000	78.317	21.32258	6.498139	30	7
2012901	6	36	0	1	850.417	0.000	38.250	32.38889	24.397046	88	0
2012901	7	31	0	8	712.200	0.000	120.949	31.06667	17.091908	66	5
2013002	1	29	0	1	420.163	0.000	12.000	31.48148	15.282897	70	5
2013002	2	81	3	5	1036.479	377.500	221.569	27.09091	13.628846	145	5
2013002	3	32	0	1	525.111	0.000	12.000	28.87500	16.163978	60	0
2013002	4	13	0	10	160.266	0.000	137.815	29.50000	23.114450	67	9
2013002	5	33	2	5	208.899	192.000	105.766	20.46154	10.974329	50	7
2013002	6	45	1	2	642.413	156.000	78.000	34.70270	22.067906	88	9
2013002	7	39	2	0	666.410	186.000	0.000	33.46154	19.704189	76	5
2013005	1	23	1	4	271.551	3.000	84.000	29.82353	13.130286	80	10
2013005	2	57	2	6	500.101	111.000	156.000	38.27273	17.718763	80	7
2013005	3	9	0	2	123.099	0.000	36.000	26.50000	21.407609	70	9
2013005	4	16	2	4	151.184	4.500	142.767	32.90000	17.922363	68	12
2013005	5	30	2	3	317.498	7.000	146.947	25.52381	10.424102	65	11
2013005	6	40	4	2	443.983	31.501	71.915	40.14815	25.673548	89	6
2013005	7	22	0	2	171.784	0.000	203.171	34.75000	16.625365	54	11

Distance between stations

Species table

Number of fish (organized by family and species) caught by Gillnet or Traps for each survey, and in total across all surveys and gears.

• (new table for revision 2020) *

##   families species
## 1       40     128

## # A tibble: 2 x 3
##   gear  families species
## * <chr>    <int>   <int>
## 1 GN          37      95
## 2 TB          19      67

fam_name	Sci_name	2012901_GN	2012901_TB	2013002_GN	2013002_TB	2013005_GN	2013005_TB	sum
ACANTHURIDAE	Acanthurus gahhm	0	14	0	29	7	55	105
ACANTHURIDAE	Acanthurus nigrofuscus	0	0	0	18	0	0	18
ALBULIDAE	Albula glossodonta	0	0	0	0	8	0	8
CARANGIDAE	Alectis indicus	0	0	0	0	2	0	2
CARANGIDAE	Alepes vari	0	0	0	0	5	0	5
SPARIDAE	Argyrops filamentosus	0	0	0	9	0	0	9
SPARIDAE	Argyrops sp.	0	25	0	0	0	0	25
SPARIDAE	Argyrops spinifer	0	0	0	9	0	10	19
ARIIDAE	Arius thalassinus	0	0	0	4	2	0	6
SCOMBRIDAE	Auxis thazard	15	0	0	0	0	0	15
BALISTIDAE	Balistapus undulatus	0	0	0	0	0	2	2
BALISTIDAE	Balistoides viridescens	0	0	0	2	0	0	2
BOTHIDAE	Bothus pantherinus	0	0	0	0	2	0	2
CAESIONIDAE	Caesio caerulaurea	0	0	17	0	0	0	17
CAESIONIDAE	Caesio suevica	0	0	0	0	2	0	2
CARANGIDAE	Carangoides armatus	0	0	0	0	5	0	5
CARANGIDAE	Carangoides bajad	16	2	11	16	62	0	107
CARANGIDAE	Carangoides ferdau	0	0	0	2	9	0	11
CARANGIDAE	Carangoides fulvoguttatus	0	0	0	1	22	0	23
CARANGIDAE	Carangoides sp.	0	0	0	0	2	0	2
CARANGIDAE	Caranx ignobilis	0	0	0	2	2	0	4
CARANGIDAE	Caranx melampygus	3	0	0	2	8	0	13
CARANGIDAE	Caranx sexfasciatus	17	0	23	3	50	0	93
CARANGIDAE	Caranx sp.	0	0	0	0	2	0	2
Carcharhinidae	Carcharhinus albimarginatus	3	0	0	0	0	0	3
Carcharhinidae	Carcharhinus melanopterus	10	1	0	0	7	0	18
Carcharhinidae	Carcharhinus wheeleri	0	0	2	0	2	0	4
ARIIDAE	Carlarius heudelotii	0	10	0	0	0	0	10
SERRANIDAE	Cephalopholis argus	0	2	0	0	0	0	2
SERRANIDAE	Cephalopholis miniatus	0	0	0	0	2	0	2
SERRANIDAE	Cephaplpholis rogaa	0	4	0	17	0	2	23
CHAETODONTIDAE	Chaetodon auriga	0	5	0	0	0	0	5
CHAETODONTIDAE	Chaetodon semilarvatus	0	0	17	3	0	0	20
CHANIDAE	Chanos chanos	0	0	0	0	2	0	2
LABRIDAE	Cheilinus lunulatus	0	0	0	2	0	0	2
LABRIDAE	Cheilinus quinquecintus	0	2	0	0	0	0	2
CHIROCENTRIDAE	Chirocentrus dorab	19	0	20	0	65	0	104
PLATYCEPHALIDAE	Cociella crocodilus	0	0	0	0	2	0	2
MUGILIDAE	Crenimugil crenilabis	0	0	2	0	0	0	2
CARANGIDAE	Decapterus macarellus	0	0	2	0	0	0	2
CARANGIDAE	Decapterus russelli	0	0	2	5	0	0	7
HAEMULIDAE	Diagramma pictum	0	2	0	0	0	0	2
DIODONTIDAE	Diodon hystrix	0	0	0	0	5	0	5
ECHENEIDIDAE	Echeneis naucrates	0	0	0	5	4	0	9
CARANGIDAE	Elagatis bipinnulata	0	0	9	0	0	0	9
SERRANIDAE	Epinephelus chlorostigma	0	0	0	2	0	0	2
SERRANIDAE	Epinephelus fasciatus	0	0	0	2	0	2	4
SERRANIDAE	Epinephelus fuscoguttatus	0	12	0	16	2	4	34
SERRANIDAE	Epinephelus summana	0	0	3	2	0	0	5
SERRANIDAE	Epinephelus tauvina	2	21	7	8	6	4	48
SCOMBRIDAE	Euthynnus affinis	0	0	5	0	4	0	9
FISTULARIIDAE	FISTULARIIDAE	10	0	0	2	0	0	12
GERREIDAE	Gerres oyena	0	0	2	0	8	0	10
CARANGIDAE	Gnathonodon speciosus	0	0	0	0	5	0	5
SCOMBRIDAE	Grammatorcynus bilineatus	29	3	8	0	8	0	48
LETHRINIDAE	Gymnocranius grandoculis	0	0	0	3	4	0	7
SCOMBRIDAE	Gymnosarda unicolor	0	0	0	0	14	0	14
MURAENIDAE	Gymnothorax flavimarginatus	0	5	0	0	0	0	5
MURAENIDAE	Gymnothorax javanicus	0	54	1	23	0	2	80
HEMIRAMPHIDAE	Hemirhamphus far	0	0	0	0	2	0	2
SCARIDAE	Hipposcarus harid	0	0	5	0	5	0	10
SCOMBRIDAE	Katsuwonus pelamis	2	0	0	0	0	0	2
KYPHOSIDAE	Kyphosus vaigiensis	0	0	0	0	13	0	13
LETHRINIDAE	Lethrinus elongatus	0	17	2	29	8	8	64
LETHRINIDAE	Lethrinus harak	0	0	0	0	12	0	12
LETHRINIDAE	Lethrinus lentjan	4	53	7	73	42	39	218
LETHRINIDAE	Lethrinus mahsena	0	30	3	36	0	28	97
LETHRINIDAE	Lethrinus microdon	0	0	2	0	0	0	2
LETHRINIDAE	Lethrinus nebulosus	0	0	0	0	0	2	2
LETHRINIDAE	Lethrinus obsoletus	0	0	2	2	0	2	6
LETHRINIDAE	Lethrinus xanthochilus	0	0	0	3	0	2	5
LUTJANIDAE	Lutjanus argentimaculatus	0	0	0	2	0	2	4
LUTJANIDAE	Lutjanus bohar	0	58	16	118	5	15	212
LUTJANIDAE	Lutjanus ehrenbergii	8	0	15	0	10	0	33
LUTJANIDAE	Lutjanus fulviflamma	0	0	0	0	3	0	3
LUTJANIDAE	Lutjanus gibbus	0	55	0	80	5	48	188
LUTJANIDAE	Lutjanus kasmira	0	7	0	6	0	4	17
LUTJANIDAE	Lutjanus monostigma	3	8	0	6	0	2	19
LUTJANIDAE	Lutjanus rivulatus	0	0	0	2	0	0	2
LUTJANIDAE	Lutjanus sebae	0	0	0	2	0	0	2
LUTJANIDAE	Lutjanus sp.	0	2	0	0	0	0	2
LUTJANIDAE	Macolor niger	0	0	0	4	2	0	6
LETHRINIDAE	Monotaxis grandoculis	0	0	2	0	0	0	2
MULLIDAE	Mulloidichtys flavolineatus	0	0	2	0	0	0	2
MULLIDAE	Mulloidichtys vanicolensis	0	0	0	0	2	0	2
HOLOCENTRIDAE	Myripristis murdjan	0	0	4	0	6	0	10
ACANTHURIDAE	Naso elegans	0	0	0	0	2	0	2
ACANTHURIDAE	Naso hexacanthus	0	0	19	19	38	0	76
NO CATCH	NO CATCH	5	0	7	0	0	0	12
LUTJANIDAE	Paracaesio sordius	0	3	0	0	0	0	3
SOLEIDAE	Pardarchius sp.	0	0	0	0	2	0	2
EPHIPPIDAE	Platax boersi	0	2	0	0	0	0	2
EPHIPPIDAE	Platax orbicularis	0	4	0	3	2	4	13
HAEMULIDAE	Plectorhinchus gaterinus	0	11	2	0	5	0	18
POMADASYIDAE (HAEMULIDAE)	Plectorhinchus pictus	0	0	0	0	2	0	2
HAEMULIDAE	Plectrohinchus pictus	0	0	0	7	2	0	9
SERRANIDAE	Plectropomus pessuliferus marisrubri	0	4	0	4	0	0	8
HAEMULIDAE	Pletrohinchus schotaf	0	0	0	0	2	0	2
PRIACANTHIDAE	Priacanthus hamrur	0	0	6	0	0	0	6
LUTJANIDAE	Pristipomoides multidens	0	14	0	9	0	0	23
BALISTIDAE	Pseudobalistes flavimarginatus	0	2	0	0	0	0	2
SCOMBRIDAE	Rastrelliger kanagurta	0	0	7	0	24	0	31
SCOMBRIDAE	Sarda orientalis	0	0	2	0	0	0	2
HOLOCENTRIDAE	Sargocentron rubrum	0	0	0	23	0	7	30
HOLOCENTRIDAE	Sargocentron spiniferum	0	24	2	26	2	10	64
SCARIDAE	Scarus ferrugineus	0	0	2	0	0	0	2
SCARIDAE	Scarus frenatus	0	0	3	0	3	0	6
SCARIDAE	Scarus ghobban	0	0	0	0	2	0	2
SCOMBRIDAE	Scomber australasicus	0	0	0	2	0	0	2
CARANGIDAE	Scomberoides lysan	17	21	66	25	53	0	182
CARANGIDAE	Scomberoides tol	0	0	5	0	77	0	82
SCOMBRIDAE	Scomberomorus commerson	39	0	0	0	12	0	51
SIGANIDAE	Siganus argenteus	0	0	4	0	0	0	4
SIGANIDAE	Siganus luridus	2	0	2	0	0	0	4
SIGANIDAE	Siganus rivulatus	0	0	0	0	2	0	2
SIGANIDAE	Siganus stellatus	0	0	2	0	0	3	5
SPARIDAE	Sparus sp.	0	0	0	7	0	0	7
SPHYRAENIDAE	Sphyraena forsteri	0	0	2	0	0	0	2
SPHYRAENIDAE	Sphyraena jello	5	0	0	0	0	0	5
SPHYRAENIDAE	Sphyraena putnamae	0	0	0	0	2	0	2
SPHYRAENIDAE	Sphyraena qenie	0	0	8	0	7	0	15
SPHYRNIDAE	Sphyrna lewini	2	0	0	0	0	0	2
R A Y S	Taeniura lymma	0	0	0	0	2	0	2
SCOMBRIDAE	Thunnus albacares	12	0	0	0	0	0	12
Carcharhinidae	Triaenodon obesus	0	5	0	17	0	0	22
BELONIDAE	Tylosurus choram	3	0	8	0	0	0	11
MUGILIDAE	Valamugil engeli	0	0	0	0	2	0	2
SERRANIDAE	Variola louti	0	0	0	4	0	0	4

fam_name	Sci_name	2012901_GN	2012901_TB	2013002_GN	2013002_TB	2013005_GN	2013005_TB	sum
ACANTHURIDAE	Acanthurus gahhm	0	4	0	5	1	6	16
ACANTHURIDAE	Acanthurus nigrofuscus	0	0	0	6	0	0	6
ALBULIDAE	Albula glossodonta	0	0	0	0	3	0	3
CARANGIDAE	Alectis indicus	0	0	0	0	1	0	1
CARANGIDAE	Alepes vari	0	0	0	0	1	0	1
SPARIDAE	Argyrops filamentosus	0	0	0	2	0	0	2
SPARIDAE	Argyrops sp.	0	5	0	0	0	0	5
SPARIDAE	Argyrops spinifer	0	0	0	3	0	5	8
ARIIDAE	Arius thalassinus	0	0	0	2	1	0	3
SCOMBRIDAE	Auxis thazard	1	0	0	0	0	0	1
BALISTIDAE	Balistapus undulatus	0	0	0	0	0	1	1
BALISTIDAE	Balistoides viridescens	0	0	0	1	0	0	1
BOTHIDAE	Bothus pantherinus	0	0	0	0	1	0	1
CAESIONIDAE	Caesio caerulaurea	0	0	2	0	0	0	2
CAESIONIDAE	Caesio suevica	0	0	0	0	1	0	1
CARANGIDAE	Carangoides armatus	0	0	0	0	1	0	1
CARANGIDAE	Carangoides bajad	3	1	5	2	13	0	24
CARANGIDAE	Carangoides ferdau	0	0	0	1	2	0	3
CARANGIDAE	Carangoides fulvoguttatus	0	0	0	1	6	0	7
CARANGIDAE	Carangoides sp.	0	0	0	0	1	0	1
CARANGIDAE	Caranx ignobilis	0	0	0	1	1	0	2
CARANGIDAE	Caranx melampygus	1	0	0	1	2	0	4
CARANGIDAE	Caranx sexfasciatus	1	0	1	1	7	0	10
CARANGIDAE	Caranx sp.	0	0	0	0	1	0	1
Carcharhinidae	Carcharhinus albimarginatus	1	0	0	0	0	0	1
Carcharhinidae	Carcharhinus melanopterus	1	1	0	0	2	0	4
Carcharhinidae	Carcharhinus wheeleri	0	0	1	0	1	0	2
ARIIDAE	Carlarius heudelotii	0	4	0	0	0	0	4
SERRANIDAE	Cephalopholis argus	0	1	0	0	0	0	1
SERRANIDAE	Cephalopholis miniatus	0	0	0	0	1	0	1
SERRANIDAE	Cephaplpholis rogaa	0	2	0	7	0	1	10
CHAETODONTIDAE	Chaetodon auriga	0	2	0	0	0	0	2
CHAETODONTIDAE	Chaetodon semilarvatus	0	0	1	1	0	0	2
CHANIDAE	Chanos chanos	0	0	0	0	1	0	1
LABRIDAE	Cheilinus lunulatus	0	0	0	1	0	0	1
LABRIDAE	Cheilinus quinquecintus	0	1	0	0	0	0	1
CHIROCENTRIDAE	Chirocentrus dorab	4	0	3	0	5	0	12
PLATYCEPHALIDAE	Cociella crocodilus	0	0	0	0	1	0	1
MUGILIDAE	Crenimugil crenilabis	0	0	1	0	0	0	1
CARANGIDAE	Decapterus macarellus	0	0	1	0	0	0	1
CARANGIDAE	Decapterus russelli	0	0	1	1	0	0	2
HAEMULIDAE	Diagramma pictum	0	1	0	0	0	0	1
DIODONTIDAE	Diodon hystrix	0	0	0	0	2	0	2
ECHENEIDIDAE	Echeneis naucrates	0	0	0	2	2	0	4
CARANGIDAE	Elagatis bipinnulata	0	0	3	0	0	0	3
SERRANIDAE	Epinephelus chlorostigma	0	0	0	1	0	0	1
SERRANIDAE	Epinephelus fasciatus	0	0	0	1	0	1	2
SERRANIDAE	Epinephelus fuscoguttatus	0	5	0	9	1	2	17
SERRANIDAE	Epinephelus summana	0	0	1	1	0	0	2
SERRANIDAE	Epinephelus tauvina	1	10	3	4	3	2	23
SCOMBRIDAE	Euthynnus affinis	0	0	1	0	1	0	2
FISTULARIIDAE	FISTULARIIDAE	1	0	0	1	0	0	2
GERREIDAE	Gerres oyena	0	0	1	0	2	0	3
CARANGIDAE	Gnathonodon speciosus	0	0	0	0	1	0	1
SCOMBRIDAE	Grammatorcynus bilineatus	3	1	3	0	3	0	10
LETHRINIDAE	Gymnocranius grandoculis	0	0	0	1	1	0	2
SCOMBRIDAE	Gymnosarda unicolor	0	0	0	0	4	0	4
MURAENIDAE	Gymnothorax flavimarginatus	0	2	0	0	0	0	2
MURAENIDAE	Gymnothorax javanicus	0	26	1	12	0	1	40
HEMIRAMPHIDAE	Hemirhamphus far	0	0	0	0	1	0	1
SCARIDAE	Hipposcarus harid	0	0	1	0	1	0	2
SCOMBRIDAE	Katsuwonus pelamis	1	0	0	0	0	0	1
KYPHOSIDAE	Kyphosus vaigiensis	0	0	0	0	3	0	3
LETHRINIDAE	Lethrinus elongatus	0	7	1	12	1	3	24
LETHRINIDAE	Lethrinus harak	0	0	0	0	2	0	2
LETHRINIDAE	Lethrinus lentjan	1	16	1	26	5	11	60
LETHRINIDAE	Lethrinus mahsena	0	14	1	17	0	10	42
LETHRINIDAE	Lethrinus microdon	0	0	1	0	0	0	1
LETHRINIDAE	Lethrinus nebulosus	0	0	0	0	0	1	1
LETHRINIDAE	Lethrinus obsoletus	0	0	1	1	0	1	3
LETHRINIDAE	Lethrinus xanthochilus	0	0	0	1	0	1	2
LUTJANIDAE	Lutjanus argentimaculatus	0	0	0	1	0	1	2
LUTJANIDAE	Lutjanus bohar	0	26	6	49	2	7	90
LUTJANIDAE	Lutjanus ehrenbergii	2	0	4	0	3	0	9
LUTJANIDAE	Lutjanus fulviflamma	0	0	0	0	1	0	1
LUTJANIDAE	Lutjanus gibbus	0	17	0	29	2	19	67
LUTJANIDAE	Lutjanus kasmira	0	3	0	3	0	2	8
LUTJANIDAE	Lutjanus monostigma	1	4	0	3	0	1	9
LUTJANIDAE	Lutjanus rivulatus	0	0	0	1	0	0	1
LUTJANIDAE	Lutjanus sebae	0	0	0	1	0	0	1
LUTJANIDAE	Lutjanus sp.	0	1	0	0	0	0	1
LUTJANIDAE	Macolor niger	0	0	0	2	1	0	3
LETHRINIDAE	Monotaxis grandoculis	0	0	1	0	0	0	1
MULLIDAE	Mulloidichtys flavolineatus	0	0	1	0	0	0	1
MULLIDAE	Mulloidichtys vanicolensis	0	0	0	0	1	0	1
HOLOCENTRIDAE	Myripristis murdjan	0	0	1	0	2	0	3
ACANTHURIDAE	Naso elegans	0	0	0	0	1	0	1
ACANTHURIDAE	Naso hexacanthus	0	0	1	1	1	0	3
NO CATCH	NO CATCH	5	0	7	0	0	0	12
LUTJANIDAE	Paracaesio sordius	0	1	0	0	0	0	1
SOLEIDAE	Pardarchius sp.	0	0	0	0	1	0	1
EPHIPPIDAE	Platax boersi	0	1	0	0	0	0	1
EPHIPPIDAE	Platax orbicularis	0	2	0	1	1	2	6
HAEMULIDAE	Plectorhinchus gaterinus	0	4	1	0	2	0	7
POMADASYIDAE (HAEMULIDAE)	Plectorhinchus pictus	0	0	0	0	1	0	1
HAEMULIDAE	Plectrohinchus pictus	0	0	0	3	1	0	4
SERRANIDAE	Plectropomus pessuliferus marisrubri	0	2	0	2	0	0	4
HAEMULIDAE	Pletrohinchus schotaf	0	0	0	0	1	0	1
PRIACANTHIDAE	Priacanthus hamrur	0	0	2	0	0	0	2
LUTJANIDAE	Pristipomoides multidens	0	5	0	2	0	0	7
BALISTIDAE	Pseudobalistes flavimarginatus	0	1	0	0	0	0	1
SCOMBRIDAE	Rastrelliger kanagurta	0	0	1	0	3	0	4
SCOMBRIDAE	Sarda orientalis	0	0	1	0	0	0	1
HOLOCENTRIDAE	Sargocentron rubrum	0	0	0	12	0	3	15
HOLOCENTRIDAE	Sargocentron spiniferum	0	11	1	13	1	5	31
SCARIDAE	Scarus ferrugineus	0	0	1	0	0	0	1
SCARIDAE	Scarus frenatus	0	0	1	0	1	0	2
SCARIDAE	Scarus ghobban	0	0	0	0	1	0	1
SCOMBRIDAE	Scomber australasicus	0	0	0	1	0	0	1
CARANGIDAE	Scomberoides lysan	2	1	6	1	8	0	18
CARANGIDAE	Scomberoides tol	0	0	1	0	3	0	4
SCOMBRIDAE	Scomberomorus commerson	2	0	0	0	3	0	5
SIGANIDAE	Siganus argenteus	0	0	1	0	0	0	1
SIGANIDAE	Siganus luridus	1	0	1	0	0	0	2
SIGANIDAE	Siganus rivulatus	0	0	0	0	1	0	1
SIGANIDAE	Siganus stellatus	0	0	1	0	0	1	2
SPARIDAE	Sparus sp.	0	0	0	1	0	0	1
SPHYRAENIDAE	Sphyraena forsteri	0	0	1	0	0	0	1
SPHYRAENIDAE	Sphyraena jello	2	0	0	0	0	0	2
SPHYRAENIDAE	Sphyraena putnamae	0	0	0	0	1	0	1
SPHYRAENIDAE	Sphyraena qenie	0	0	2	0	3	0	5
SPHYRNIDAE	Sphyrna lewini	1	0	0	0	0	0	1
R A Y S	Taeniura lymma	0	0	0	0	1	0	1
SCOMBRIDAE	Thunnus albacares	1	0	0	0	0	0	1
Carcharhinidae	Triaenodon obesus	0	2	0	7	0	0	9
BELONIDAE	Tylosurus choram	1	0	4	0	0	0	5
MUGILIDAE	Valamugil engeli	0	0	0	0	1	0	1
SERRANIDAE	Variola louti	0	0	0	2	0	0	2

---

Number of set traps per depth

Box plot of depths at wich traps were set. I prefer this visualization of depth ranges of the traps, rather than a (boring) table.

####Test differneces in depths in each area between years and between areas

## 
##  Shapiro-Wilk normality test
## 
## data:  c3$depth
## W = 0.90427, p-value < 2.2e-16

## 
##  Kruskal-Wallis rank sum test
## 
## data:  c3$depth and c3$sa
## Kruskal-Wallis chi-squared = 53.895, df = 20, p-value = 5.997e-05

##   Kruskal-Wallis rank sum test
## 
## data: x and group
## Kruskal-Wallis chi-squared = 53.8945, df = 20, p-value = 0
## 
## 
##                            Comparison of x by group                            
##                                     (Holm)                                     
## Col Mean-|
## Row Mean |    May13A1    May13A2    May13A3    May13A4    May13A5    May13A6
## ---------+------------------------------------------------------------------
##  May13A2 |   0.584662
##          |     1.0000
##          |
##  May13A3 |   0.726734   0.286418
##          |     1.0000     1.0000
##          |
##  May13A4 |   0.091249  -0.321520  -0.473906
##          |     1.0000     1.0000     1.0000
##          |
##  May13A5 |   2.565797   2.549703   1.881309   1.901388
##          |     0.9832     1.0000     1.0000     1.0000
##          |
##  May13A6 |  -0.635001  -1.493788  -1.459668  -0.576956  -3.509345
##          |     1.0000     1.0000     1.0000     1.0000     0.0492
##          |
##  May13A7 |  -0.212107  -0.915996  -0.999219  -0.257499  -2.981008   0.453441
##          |     1.0000     1.0000     1.0000     1.0000     0.2950     1.0000
##          |
##  Nov12A1 |  -0.961309  -1.656760  -1.654111  -0.864001  -3.360207  -0.463510
##          |     1.0000     1.0000     1.0000     1.0000     0.0837     1.0000
##          |
##  Nov12A2 |  -1.842857  -3.135111  -2.736966  -1.471887  -4.875796  -1.352787
##          |     1.0000     0.1795     0.6179     1.0000    0.0001*     1.0000
##          |
##  Nov12A3 |   0.559876   0.109546  -0.132979   0.355495  -1.913820   1.227666
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A4 |   1.575827   1.381371   1.199344   1.392178   0.229206   1.939483
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A5 |   2.538090   2.505617   1.862533   1.892225   0.010486   3.457040
##          |     1.0000     1.0000     1.0000     1.0000     0.4958     0.0593
##          |
##  Nov12A6 |   0.990507   0.602230   0.250369   0.669691  -1.684325   1.781536
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A7 |   0.479086  -0.013029  -0.248225   0.282398  -2.116187   1.178097
##          |     1.0000     0.9896     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A1 |  -0.290758  -0.879093  -0.978572  -0.321741  -2.703183   0.273202
##          |     1.0000     1.0000     1.0000     1.0000     0.6765     1.0000
##          |
##  Nov13A2 |  -0.901542  -1.921244  -1.774427  -0.768143  -3.925721  -0.272921
##          |     1.0000     1.0000     1.0000     1.0000    0.0099*     1.0000
##          |
##  Nov13A3 |   0.472943   0.153515  -0.015539   0.345925  -1.256781   0.908320
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A4 |  -0.028629  -0.495052  -0.637647  -0.105444  -2.173051   0.488869
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |   2.011625   1.859094   1.328184   1.485929  -0.512227   2.864077
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.4230
##          |
##  Nov13A6 |   0.051337  -0.589905  -0.732814  -0.056180  -2.723833   0.753462
##          |     1.0000     1.0000     1.0000     1.0000     0.6394     1.0000
##          |
##  Nov13A7 |  -0.252525  -0.823225  -0.930546  -0.291156  -2.632133   0.306804
##          |     1.0000     1.0000     1.0000     1.0000     0.8208     1.0000
## Col Mean-|
## Row Mean |    May13A7    Nov12A1    Nov12A2    Nov12A3    Nov12A4    Nov12A5
## ---------+------------------------------------------------------------------
##  Nov12A1 |  -0.824279
##          |     1.0000
##          |
##  Nov12A2 |  -1.771440  -0.602817
##          |     1.0000     1.0000
##          |
##  Nov12A3 |   0.802661   1.460237   2.410822
##          |     1.0000     1.0000     1.0000
##          |
##  Nov12A4 |   1.715894   2.088801   2.539967   1.252970
##          |     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A5 |   2.941147   3.327080   4.792649   1.897044  -0.222797
##          |     0.3335     0.0936    0.0002*     1.0000     1.0000
##          |
##  Nov12A6 |   1.294369   1.917652   3.120454   0.372768  -1.080997  -1.667378
##          |     1.0000     1.0000     0.1876     1.0000     1.0000     1.0000
##          |
##  Nov12A7 |   0.730511   1.418952   2.432055  -0.103200  -1.326539  -2.094203
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A1 |  -0.110971   0.639163   1.377873  -0.811862  -1.710971  -2.677596
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.7222
##          |
##  Nov13A2 |  -0.739266   0.263578   1.151267  -1.507887  -2.076913  -3.859649
##          |     1.0000     1.0000     1.0000     1.0000     1.0000    0.0129*
##          |
##  Nov13A3 |   0.628634   1.142965   1.679593   0.075624  -1.044527  -1.255110
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A4 |   0.145151   0.800077   1.459213  -0.503953  -1.506761  -2.159038
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |   2.371322   2.834606   4.163719   1.390749  -0.495215  -0.514777
##          |     1.0000     0.4612    0.0037*     1.0000     1.0000     1.0000
##          |
##  Nov13A6 |   0.286754   1.071132   2.093771  -0.550548  -1.582295  -2.687889
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.7042
##          |
##  Nov13A7 |  -0.072712   0.664637   1.395117  -0.768460  -1.684315  -2.608222
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.8751
## Col Mean-|
## Row Mean |    Nov12A6    Nov12A7    Nov13A1    Nov13A2    Nov13A3    Nov13A4
## ---------+------------------------------------------------------------------
##  Nov12A7 |  -0.503561
##          |     1.0000
##          |
##  Nov13A1 |  -1.230007  -0.744733
##          |     1.0000     1.0000
##          |
##  Nov13A2 |  -2.126875  -1.476067  -0.503799
##          |     1.0000     1.0000     1.0000
##          |
##  Nov13A3 |  -0.178953   0.149724   0.665458   1.076420
##          |     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A4 |  -0.852245  -0.431033   0.221992   0.695320  -0.454502
##          |     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |   1.119330   1.562189   2.183092   3.234149   0.903539   1.721014
##          |     1.0000     1.0000     1.0000     0.1288     1.0000     1.0000
##          |
##  Nov13A6 |  -1.021316  -0.464913   0.358085   1.057670  -0.455203   0.072469
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A7 |  -1.177131  -0.700095   0.032818   0.534827  -0.636511  -0.190164
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
## Col Mean-|
## Row Mean |    Nov13A5    Nov13A6
## ---------+----------------------
##  Nov13A6 |  -2.117136
##          |     1.0000
##          |
##  Nov13A7 |  -2.120671  -0.316155
##          |     1.0000     1.0000
## 
## alpha = 0.05
## Reject Ho if p <= alpha/2

	May 2013							Nov 2012							Nov 2013
	May13A1	May13A2	May13A3	May13A4	May13A5	May13A6	May13A7	Nov12A1	Nov12A2	Nov12A3	Nov12A4	Nov12A5	Nov12A6	Nov12A7	Nov13A1	Nov13A2	Nov13A3	Nov13A4	Nov13A5	Nov13A6	Nov13A7
May13A1
May13A2	1.00000
May13A3	1.00000	1.00000
May13A4	1.00000	1.00000	1.00000
May13A5	0.98324	1.00000	1.00000	1.00000
May13A6	1.00000	1.00000	1.00000	1.00000	0.04921
May13A7	1.00000	1.00000	1.00000	1.00000	0.29501	1.00000
Nov12A1	1.00000	1.00000	1.00000	1.00000	0.08366	1.00000	1.00000
Nov12A2	1.00000	0.1795	0.61785	1.00000	0.00014	1.00000	1.00000	1.00000
Nov12A3	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A4	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A5	1.00000	1.00000	1.00000	1.00000	0.49582	0.05932	0.3335	0.09361	0.00021	1.00000	1.00000
Nov12A6	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.18759	1.00000	1.00000	1.00000
Nov12A7	1.00000	0.98961	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A1	1.00000	1.00000	1.00000	1.00000	0.67647	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.72216	1.00000	1.00000
Nov13A2	1.00000	1.00000	1.00000	1.00000	0.0099	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.01286	1.00000	1.00000	1.00000
Nov13A3	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A4	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A5	1.00000	1.00000	1.00000	1.00000	1.00000	0.42302	1.00000	0.46118	0.00369	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.1288	1.00000	1.00000
Nov13A6	1.00000	1.00000	1.00000	1.00000	0.63936	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.7042	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A7	1.00000	1.00000	1.00000	1.00000	0.82079	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.8751	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000

Depth data were not normally distributed and hence the difference in depth could only be analyzed using non-parametric Kruskal - Wallis rank sum test, and Conover - Iman post hoc test with Holm-Bonferroni correction.

Of the 210 survey - area comparisons, depth distributions were significantly different between areas as follows:

May13A5 - Nov12A2
May13A5 - Nov13A2
Nov12A2 - Nov12A5
Nov12A5 - Nov13A2
Nov12A2 - Nov13A5

Which corresponds with the plot of depth-distributions by survey and area above.

---

Duration of trap sets

Mean, min & max hrs. of fishing

id	maxhrs	minhrs	meanhrs
1	46.417	11.500	20.48641
2	44.500	7.917	16.30586
3	24.250	8.167	16.55437
4	27.033	13.033	16.11076
5	40.200	11.667	17.79179
6	30.083	8.000	19.00436
7	31.866	10.417	19.47891

##        ss                  id             fhrs       
##  Min.   :2.013e+09   Min.   :1.000   Min.   : 7.917  
##  1st Qu.:2.013e+09   1st Qu.:2.000   1st Qu.:15.404  
##  Median :2.013e+09   Median :4.000   Median :16.133  
##  Mean   :2.013e+09   Mean   :3.909   Mean   :17.905  
##  3rd Qu.:2.013e+09   3rd Qu.:6.000   3rd Qu.:18.637  
##  Max.   :2.013e+09   Max.   :7.000   Max.   :46.417

Fishing hours (traps) by survey and area

Boxplot of median with mean values plotted as red circle. The average trap soak-time (fishing hours) across all surveys and areas was 17.984 hrs with a median of 16.15, but the November 2012 survey had more varied and higher soak times than the May 2013 and Nov. 2013.

Test differences in soak times (traps) K-W test

## 
##  Shapiro-Wilk normality test
## 
## data:  c3$Fhrs
## W = 0.66295, p-value < 2.2e-16

## 
##  Kruskal-Wallis rank sum test
## 
## data:  c3$Fhrs and c3$sa
## Kruskal-Wallis chi-squared = 233.68, df = 20, p-value < 2.2e-16

##   Kruskal-Wallis rank sum test
## 
## data: x and group
## Kruskal-Wallis chi-squared = 233.6845, df = 20, p-value = 0
## 
## 
##                            Comparison of x by group                            
##                                     (Holm)                                     
## Col Mean-|
## Row Mean |    May13A1    May13A2    May13A3    May13A4    May13A5    May13A6
## ---------+------------------------------------------------------------------
##  May13A2 |   0.019167
##          |     1.0000
##          |
##  May13A3 |  -1.845687  -2.286216
##          |     1.0000     1.0000
##          |
##  May13A4 |  -0.320485  -0.371906   1.113524
##          |     1.0000     1.0000     1.0000
##          |
##  May13A5 |  -2.246923  -2.789417  -0.397848  -1.419865
##          |     1.0000     0.3043     1.0000     1.0000
##          |
##  May13A6 |  -4.244256  -5.458261  -2.324296  -2.870062  -1.914471
##          |    0.0019*    0.0000*     0.9901     0.2478     1.0000
##          |
##  May13A7 |  -3.405686  -4.305897  -1.517177  -2.273520  -1.112647   0.802631
##          |     0.0466    0.0015*     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A1 |  -4.829058  -5.696259  -3.221177  -3.597107  -2.882630  -1.363272
##          |    0.0001*    0.0000*     0.0858    0.0237*     0.2424     1.0000
##          |
##  Nov12A2 |  -4.705998  -6.190516  -2.735283  -3.160669  -2.314961  -0.360371
##          |    0.0002*    0.0000*     0.3489     0.1013     1.0000     1.0000
##          |
##  Nov12A3 |  -3.182602  -3.831827  -1.463318  -2.215575  -1.096949   0.613429
##          |     0.0963    0.0101*     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A4 |   1.010214   1.052566   2.001281   1.132860   2.211071   3.181677
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.0959
##          |
##  Nov12A5 |  -5.773651  -7.082175  -4.041039  -4.190380  -3.676878  -2.060333
##          |    0.0000*    0.0000*    0.0045*    0.0024*    0.0179*     1.0000
##          |
##  Nov12A6 |  -8.907780  -11.11697  -7.200759  -6.538527  -6.849655  -5.420221
##          |    0.0000*    0.0000*    0.0000*    0.0000*    0.0000*    0.0000*
##          |
##  Nov12A7 |  -8.795131  -10.77815  -7.138466  -6.552716  -6.797659  -5.404561
##          |    0.0000*    0.0000*    0.0000*    0.0000*    0.0000*    0.0000*
##          |
##  Nov13A1 |  -2.040583  -2.429030  -0.353216  -1.333735   0.007911   1.720167
##          |     1.0000     0.7858     1.0000     1.0000     0.9937     1.0000
##          |
##  Nov13A2 |   0.181700   0.215731   2.329854   0.482877   2.804046   5.276055
##          |     1.0000     1.0000     0.9858     1.0000     0.2936    0.0000*
##          |
##  Nov13A3 |   1.274552   1.372309   2.543103   1.368215   2.814084   4.099715
##          |     1.0000     1.0000     0.5833     1.0000     0.2872    0.0035*
##          |
##  Nov13A4 |  -0.556590  -0.648738   0.979374  -0.177731   1.308371   2.876766
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.2448
##          |
##  Nov13A5 |   0.531164   0.627791   2.406335   0.738726   2.815455   4.874793
##          |     1.0000     1.0000     0.8277     1.0000     0.2885    0.0001*
##          |
##  Nov13A6 |  -1.353988  -1.730259   0.602853  -0.699303   1.027721   3.131292
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.1109
##          |
##  Nov13A7 |  -3.296405  -3.893848  -1.656560  -2.358403  -1.308265   0.302434
##          |     0.0671    0.0080*     1.0000     0.9230     1.0000     1.0000
## Col Mean-|
## Row Mean |    May13A7    Nov12A1    Nov12A2    Nov12A3    Nov12A4    Nov12A5
## ---------+------------------------------------------------------------------
##  Nov12A1 |  -1.988652
##          |     1.0000
##          |
##  Nov12A2 |  -1.181754   1.114595
##          |     1.0000     1.0000
##          |
##  Nov12A3 |  -0.096705   1.745926   0.937774
##          |     1.0000     1.0000     1.0000
##          |
##  Nov12A4 |   2.787816   3.743203   3.364131   2.761979
##          |     0.3031    0.0140*     0.0536     0.3249
##          |
##  Nov12A5 |  -2.728342  -0.452885  -1.811352  -2.376609  -4.110043
##          |     0.3530     1.0000     1.0000     0.8880    0.0034*
##          |
##  Nov12A6 |  -6.003656  -3.168143  -5.294800  -5.296309  -5.682125  -2.983793
##          |    0.0000*     0.0996    0.0000*    0.0000*    0.0000*     0.1772
##          |
##  Nov12A7 |  -5.972333  -3.252960  -5.275400  -5.311863  -5.729726  -3.073133
##          |    0.0000*     0.0775    0.0000*    0.0000*    0.0000*     0.1335
##          |
##  Nov13A1 |   1.009175   2.667754   2.062936   1.012405  -2.146061   3.349600
##          |     1.0000     0.4147     1.0000     1.0000     1.0000     0.0561
##          |
##  Nov13A2 |   4.217915   5.604789   5.923402   3.807271  -0.959949   6.869511
##          |    0.0022*    0.0000*    0.0000*    0.0110*     1.0000    0.0000*
##          |
##  Nov13A3 |   3.573301   4.679637   4.359899   3.480038  -0.005114   5.223697
##          |     0.0255    0.0003*    0.0012*     0.0358     0.4980    0.0000*
##          |
##  Nov13A4 |   2.228958   3.627888   3.197334   2.159691  -1.293095   4.282409
##          |     1.0000    0.0214*     0.0923     1.0000     1.0000    0.0017*
##          |
##  Nov13A5 |   4.008306   5.356950   5.365326   3.724214  -0.726343   6.364118
##          |    0.0051*    0.0000*    0.0000*    0.0150*     1.0000    0.0000*
##          |
##  Nov13A6 |   2.243440   3.899681   3.610500   2.101251  -1.727459   4.853401
##          |     1.0000    0.0079*    0.0227*     1.0000     1.0000    0.0001*
##          |
##  Nov13A7 |  -0.363487   1.437192   0.598651  -0.250049  -2.868556   2.007320
##          |     1.0000     1.0000     1.0000     1.0000     0.2469     1.0000
## Col Mean-|
## Row Mean |    Nov12A6    Nov12A7    Nov13A1    Nov13A2    Nov13A3    Nov13A4
## ---------+------------------------------------------------------------------
##  Nov12A7 |  -0.201945
##          |     1.0000
##          |
##  Nov13A1 |   6.192118   6.185973
##          |    0.0000*    0.0000*
##          |
##  Nov13A2 |   10.63522   10.36729   2.474252
##          |    0.0000*    0.0000*     0.7006
##          |
##  Nov13A3 |   7.269017   7.285218   2.686030   1.240317
##          |    0.0000*    0.0000*     0.3967     1.0000
##          |
##  Nov13A4 |   6.820619   6.818165   1.217743  -0.759148  -1.583188
##          |    0.0000*    0.0000*     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |   9.550708   9.411957   2.554888   0.429503  -0.915660   1.006743
##          |    0.0000*    0.0000*     0.5695     1.0000     1.0000     1.0000
##          |
##  Nov13A6 |   8.237562   8.115501   0.915365  -1.801935  -2.213287  -0.530390
##          |    0.0000*    0.0000*     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A7 |   4.769422   4.807396  -1.214680  -3.878340  -3.584495  -2.309030
##          |    0.0002*    0.0002*     1.0000    0.0085*    0.0247*     1.0000
## Col Mean-|
## Row Mean |    Nov13A5    Nov13A6
## ---------+----------------------
##  Nov13A6 |  -1.939976
##          |     1.0000
##          |
##  Nov13A7 |  -3.813158  -2.267140
##          |    0.0108*     1.0000
## 
## alpha = 0.05
## Reject Ho if p <= alpha/2

	May 2013							Nov 2012							Nov 2013
	May13A1	May13A2	May13A3	May13A4	May13A5	May13A6	May13A7	Nov12A1	Nov12A2	Nov12A3	Nov12A4	Nov12A5	Nov12A6	Nov12A7	Nov13A1	Nov13A2	Nov13A3	Nov13A4	Nov13A5	Nov13A6	Nov13A7
May13A1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A3	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A4	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A5	1	0.304318257715511	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A6	0.00194605627325626	6.0153516764845e-06	0.99014924726454	0.247846643571116	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
May13A7	0.0465786037953744	0.00150608523631254	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Nov12A1	0.000139401665750064	1.65841091739907e-06	0.085762577046115	0.0237070897308893	0.242364685182563	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Nov12A2	0.000246854236398083	9.85544426575519e-08	0.348903164449488	0.10128218484263	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0
Nov12A3	0.0963172984556005	0.01011222165344	1	1	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0
Nov12A4	1	1	1	1	1	0.0958539432717497	0.303077471568711	0.0140370471898213	0.0536425536268057	0.324874306066398	0	0	0	0	0	0	0	0	0	0	0
Nov12A5	1.08576347948883e-06	3.60740229365083e-10	0.00446641785383584	0.00242370829284479	0.017886611574173	1	0.352976745386185	1	1	0.887992322275862	0.00338945629930982	0	0	0	0	0	0	0	0	0	0
Nov12A6	5.2359286286373e-16	2.1540429044105e-24	1.63692763192675e-10	1.18467837523924e-08	1.66133967069069e-09	7.33635323109579e-06	2.94496149948804e-07	0.0995555306130913	1.37718820396049e-05	1.37441209615493e-05	1.78443719959448e-06	0.177195831523622	0	0	0	0	0	0	0	0	0
Nov12A7	1.28323842907066e-15	5.05069713249247e-23	2.48314927654109e-10	1.08942395287012e-08	2.29341629103919e-09	7.93138710985162e-06	3.51537134476469e-07	0.0774601726513087	1.50645764740843e-05	1.27400551234501e-05	1.38274101388467e-06	0.13349042546484	1	0	0	0	0	0	0	0	0
Nov13A1	1	0.785810264447672	1	1	0.993689758253955	1	1	0.414747618618474	1	1	1	0.0560583239365252	9.81378305516571e-08	1.00729048552809e-07	0	0	0	0	0	0	0
Nov13A2	1	1	0.985768196167901	1	0.293604450979064	1.51029235971449e-05	0.00216693043064203	2.72354358587187e-06	4.64196786421573e-07	0.0109925087133428	1	1.46686440652933e-09	1.86845271596954e-22	2.10885855166046e-21	0.700634336679504	0	0	0	0	0	0
Nov13A3	1	1	0.583274682919207	1	0.287217829755948	0.00351701983439009	0.025546838872539	0.000277886368368139	0.00119407105254703	0.0358253163893824	0.497960567181018	1.96014357639613e-05	1.03279080058057e-10	9.2889817131846e-11	0.396675142409312	1	0	0	0	0	0
Nov13A4	1	1	1	1	1	0.244781715563496	1	0.021410245703009	0.0923298516429121	1	1	0.00165873956582547	1.99665065351593e-09	2.0182259660534e-09	1	1	1	0	0	0	0
Nov13A5	1	1	0.827706966144769	1	0.288518841586274	0.000112949580366922	0.0050814730983063	1.00998716838306e-05	9.71726948443586e-06	0.0150064118947393	1	3.47698121001712e-08	2.61679204699592e-18	8.38112350192771e-18	0.569510613425275	1	1	1	0	0	0
Nov13A6	1	1	1	1	1	0.110904558548165	1	0.00786507176793555	0.0227032041040827	1	1	0.00012461267792295	9.70357762936814e-14	2.41915146938145e-13	1	1	1	1	1	0	0
Nov13A7	0.0671026136595497	0.00799786259994307	1	0.923049974115501	1	1	1	1	1	1	0.246886570042301	1	0.000183565624161181	0.000153855187385531	1	0.00845489896960382	0.0246776078168709	1	0.0108145580268575	1	0

Trap soak times:
Of the 210 area- survey combinations 69 were significantly different (Kruskal Wallis rank sum test with Conover-Iman post-hoc test w Holm correction, p<0.05):

May13A1 - May13A6
May13A1 - Nov12A1
May13A1 - Nov12A2
May13A1 - Nov12A5
May13A1 - Nov12A6
May13A1 - Nov12A7

May13A2 - May13A6
May13A2 - May13A7
May13A2 - Nov12A1
May13A2 - Nov12A2
May13A2 - Nov12A5
May13A2 - Nov12A6
May13A2 - Nov12A7

May13A3 - Nov12A6
May13A3 - Nov12A7

May13A4 - Nov12A1
May13A4 - Nov12A5
May13A4 - Nov12A6
May13A4 - Nov12A7

May13A5 - Nov12A5
May13A5 - Nov12A6
May13A5 - Nov12A7

May13A6 - Nov12A6
May13A6 - Nov12A7
May13A6 - Nov13A2
May13A6 - Nov13A3
May13A6 - Nov13A5

May13A7 - Nov12A6
May13A7 - Nov12A7
May13A7 - Nov13A2
May13A7 - Nov13A5

Nov12A1 - Nov12A4
Nov12A1 - Nov13A2
Nov12A1 - Nov13A3
Nov12A1 - Nov13A4
Nov12A1 - Nov13A5
Nov12A1 - Nov13A6

Nov12A2 - Nov12A6
Nov12A2 - Nov12A7
Nov12A2 - Nov13A2
Nov12A2 - Nov13A3
Nov12A2 - Nov13A5

Nov12A3 - Nov12A6
Nov12A3 - Nov12A7
Nov12A3 - Nov13A5

Nov12A4 - Nov12A5
Nov12A4 - Nov12A6
Nov12A4 - Nov12A7

Nov12A5 - Nov13A2
Nov12A5 - Nov13A3
Nov12A5 - Nov13A4
Nov12A5 - Nov13A5
Nov12A5 - Nov13A6

Nov12A6 - Nov13A1
Nov12A6 - Nov13A2
Nov12A6 - Nov13A3
Nov12A6 - Nov13A4
Nov12A6 - Nov13A5
Nov12A6 - Nov13A6
Nov12A6 - Nov13A7

Nov12A7 - Nov13A1
Nov12A7 - Nov13A2
Nov12A7 - Nov13A3
Nov12A7 - Nov13A4
Nov12A7 - Nov13A5
Nov12A7 - Nov13A6
Nov12A7 - Nov13A7

Nov13A1 - Nov13A7

Nov13A5 - Nov13A7

eys.

Duration and catch rates of gill-net sets

Fishing hours for traps was significantly affected by areas (except area 3 which had a very large variability), however, the number of gillnet sets was very low pr area and survey.

Test of difference in fishing hours, gillnets

##   Kruskal-Wallis rank sum test
## 
## data: x and group
## Kruskal-Wallis chi-squared = 29.8951, df = 17, p-value = 0.03
## 
## 
##                            Comparison of x by group                            
##                                     (Holm)                                     
## Col Mean-|
## Row Mean |    May13A1    May13A2    May13A3    May13A4    May13A5    May13A6
## ---------+------------------------------------------------------------------
##  May13A2 |  -2.352198
##          |     1.0000
##          |
##  May13A3 |   0.000000   2.352198
##          |     1.0000     1.0000
##          |
##  May13A4 |  -2.042245   0.793793  -2.042245
##          |     1.0000     1.0000     1.0000
##          |
##  May13A5 |  -0.542814   3.133943  -0.542814   2.824972
##          |     1.0000     0.2265     1.0000     0.5123
##          |
##  May13A6 |  -0.770026   1.952546  -0.770026   1.547694  -0.416492
##          |     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A2 |  -0.664463   2.477687  -0.664463   2.088271  -0.236387   0.192611
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A3 |  -1.130276   0.893018  -1.130276   0.518181  -0.916365  -0.535103
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A5 |  -1.193798   1.851459  -1.193798   1.364447  -1.103250  -0.452204
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A6 |  -0.632954   1.535057  -0.632954   1.188769  -0.274325   0.039153
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov12A7 |  -0.915523   2.816487  -0.915523   2.468401  -0.660311  -0.035383
##          |     1.0000     0.5204     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A1 |   0.000000   3.841123   0.000000   3.620514   0.886413   1.088982
##          |     1.0000     0.0285     1.0000     0.0555     1.0000     1.0000
##          |
##  Nov13A2 |  -0.665945   3.067396  -0.665945   2.754895  -0.205900   0.274077
##          |     1.0000     0.2691     1.0000     0.6036     1.0000     1.0000
##          |
##  Nov13A3 |   0.000000   3.079750   0.000000   2.765213   0.710711   0.943086
##          |     1.0000     0.2617     1.0000     0.5916     1.0000     1.0000
##          |
##  Nov13A4 |  -0.750591   2.590137  -0.750591   2.202029  -0.364573   0.119972
##          |     1.0000     0.9093     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |  -1.974934   0.405652  -1.974934  -0.210445  -2.308422  -1.465016
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A6 |  -1.487848   0.901763  -1.487848   0.412718  -1.467275  -0.879148
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A7 |  -1.866336   0.347713  -1.866336  -0.185723  -2.021325  -1.342699
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
## Col Mean-|
## Row Mean |    Nov12A2    Nov12A3    Nov12A5    Nov12A6    Nov12A7    Nov13A1
## ---------+------------------------------------------------------------------
##  Nov12A3 |  -0.719835
##          |     1.0000
##          |
##  Nov12A5 |  -0.742966   0.235900
##          |     1.0000     1.0000
##          |
##  Nov12A6 |  -0.110743   0.497321   0.393167
##          |     1.0000     1.0000     1.0000
##          |
##  Nov12A7 |  -0.301036   0.591511   0.593834  -0.071584
##          |     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A1 |   1.004574   1.429698   1.887560   0.800631   1.585733
##          |     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A2 |   0.067816   0.813933   0.953379   0.162786   0.466161  -1.114339
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A3 |   0.840487   1.305130   1.541187   0.730872   1.228303   0.000000
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.5000
##          |
##  Nov13A4 |  -0.094178   0.679106   0.700771   0.050039   0.215346  -1.186789
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A5 |  -1.853285  -0.590634  -1.238278  -1.199726  -1.934109  -2.985820
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.3345
##          |
##  Nov13A6 |  -1.155670  -0.182718  -0.562948  -0.756975  -1.076661  -2.104135
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A7 |  -1.663465  -0.561206  -1.098211  -1.135463  -1.663012  -2.639398
##          |     1.0000     1.0000     1.0000     1.0000     1.0000     0.8080
## Col Mean-|
## Row Mean |    Nov13A2    Nov13A3    Nov13A4    Nov13A5    Nov13A6
## ---------+-------------------------------------------------------
##  Nov13A3 |   0.880962
##          |     1.0000
##          |
##  Nov13A4 |  -0.185723  -0.969009
##          |     1.0000     1.0000
##          |
##  Nov13A5 |  -2.207805  -2.498116  -1.887066
##          |     1.0000     1.0000     1.0000
##          |
##  Nov13A6 |  -1.350809  -1.822234  -1.135125   0.501957
##          |     1.0000     1.0000     1.0000     1.0000
##          |
##  Nov13A7 |  -1.918541  -2.285785  -1.670388  -0.005836  -0.463550
##          |     1.0000     1.0000     1.0000     1.0000     1.0000
## 
## alpha = 0.05
## Reject Ho if p <= alpha/2

	May 2013						Nov 2012					Nov 2013
	May13A1	May13A2	May13A3	May13A4	May13A5	May13A6	Nov12A2	Nov12A3	Nov12A5	Nov12A6	Nov12A7	Nov13A1	Nov13A2	Nov13A3	Nov13A4	Nov13A5	Nov13A6	Nov13A7
May13A1
May13A2	1.00000
May13A3	1.00000	1.00000
May13A4	1.00000	1.00000	1.00000
May13A5	1.00000	0.22653	1.00000	0.51231
May13A6	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A2	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A3	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A5	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A6	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov12A7	1.00000	0.52039	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A1	1.00000	0.02855	1.00000	0.05548	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A2	1.00000	0.26906	1.00000	0.60359	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A3	1.00000	0.26174	1.00000	0.59158	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.5	1.00000
Nov13A4	1.00000	0.9093	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A5	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.3345	1.00000	1.00000	1.00000
Nov13A6	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
Nov13A7	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000	0.80797	1.00000	1.00000	1.00000	1.00000	1.00000

Gill net fishing time:
K-W test significant, but only 2 were significant (Conover-Iman post-hoc test w Holm correction, p<0.05):

Nov13A1 - May13A2
Nov13A1 - May13A5

---

Analysis of catches by traits and other factors

Catch rates pr area & survey

Must aggregate data per station

Statistical analysis CPUE catches

Testing for normality of CPUEw of trap catches

Shapiro test for normality and Q-Q plots shows that data is non-normal and zero-inflated.

## 
##  Shapiro-Wilk normality test
## 
## data:  cpue.tr.st$CPUEw
## W = 0.55152, p-value < 2.2e-16

### Test for difference in CPUEw of traps between areas (non-parametric test)

CPUE data are non-normal (see Shapiro-test & Q-Q plots above) & zero-inflated (many stations with 0 catch).

Zero-inflated GAM model A GAM model approach is more appropriate than a non-parametric approach (e.g Kruskal Wallis) as it evaluates the effects of all factors at the same time, instead of evaluating the effects of area separate from survey.

• Developed seperate trap and gillnet models for CPUE (weight and numbers) including depth, area and survey.

• Tested models where survey was included as a random factor, although this excludes evaluation of the potential significant effects of the surveys on the results.

• Tested wether specifying the nu-function improved the fit (reduced the AIC)

• Use Zero-inflated distributions to fit the model (available in the GAMLSS package)

GAM model CPUE - traps only

Statistical analysis of plot of trap catch rates.

Because traps and gillnets are very different & their deployment is confounded with depth (gillnets fish at surface, traps fishat bottom) it is not appropriate to combine both gear into a single model, they must be evaluated seperately.

## GAMLSS-RS iteration 1: Global Deviance = 681.6152 
## GAMLSS-RS iteration 2: Global Deviance = 681.6145

## GAMLSS-RS iteration 1: Global Deviance = 681.3479 
## GAMLSS-RS iteration 2: Global Deviance = 681.3479

## GAMLSS-RS iteration 1: Global Deviance = 396.3758 
## GAMLSS-RS iteration 2: Global Deviance = 396.3741 
## GAMLSS-RS iteration 3: Global Deviance = 396.3741

## GAMLSS-RS iteration 1: Global Deviance = 395.0602 
## GAMLSS-RS iteration 2: Global Deviance = 395.0599

## GAMLSS-RS iteration 1: Global Deviance = 640.1004 
## GAMLSS-RS iteration 2: Global Deviance = 640.1004

## GAMLSS-RS iteration 1: Global Deviance = 358.2109 
## GAMLSS-RS iteration 2: Global Deviance = 358.2106

##              df      AIC
## mod555 21.00000 400.2106
## mod55  12.00000 419.0599
## mod44  11.82553 420.0252
## mod333 21.00000 682.1004
## mod33  12.00000 705.3479
## mod22  12.66640 706.9473

## ******************************************************************
## Family:  c("ZAGA", "Zero Adjusted GA") 
## 
## Call:  gamlss(formula = CPUEn ~ depth + factor(id) + factor(survey),  
##     nu.formula = ~depth + factor(id) + factor(survey),  
##     family = ZAGA, data = c22) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -2.1133836  0.1931986 -10.939  < 2e-16 ***
## depth                 -0.0009092  0.0025924  -0.351  0.72592    
## factor(id)2            0.2896902  0.1674014   1.731  0.08401 .  
## factor(id)3            0.1802016  0.2141091   0.842  0.40030    
## factor(id)4           -0.0014326  0.2581430  -0.006  0.99557    
## factor(id)5            0.5108807  0.2032557   2.513  0.01220 *  
## factor(id)6           -0.2143588  0.1778905  -1.205  0.22864    
## factor(id)7            0.1928098  0.1960893   0.983  0.32584    
## factor(survey)2013002  0.3019887  0.1084315   2.785  0.00551 ** 
## factor(survey)2013005  0.2914302  0.1371937   2.124  0.03403 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.2072     0.0367  -5.645 2.47e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Nu link function:  logit 
## Nu Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.0815597  0.3066923  -0.266   0.7904    
## depth                 -0.0006377  0.0043131  -0.148   0.8825    
## factor(id)2           -0.0416457  0.2810371  -0.148   0.8822    
## factor(id)3            0.2110005  0.3472408   0.608   0.5436    
## factor(id)4           -0.1382635  0.4287207  -0.323   0.7472    
## factor(id)5            0.2580592  0.3275442   0.788   0.4311    
## factor(id)6           -0.5981704  0.3069647  -1.949   0.0518 .  
## factor(id)7            0.1570886  0.3217318   0.488   0.6255    
## factor(survey)2013002  0.0848371  0.1881020   0.451   0.6521    
## factor(survey)2013005  0.9733749  0.2129305   4.571  5.8e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  670 
## Degrees of Freedom for the fit:  21
##       Residual Deg. of Freedom:  649 
##                       at cycle:  2 
##  
## Global Deviance:     358.2106 
##             AIC:     400.2106 
##             SBC:     494.8635 
## ******************************************************************

## Single term deletions for
## mu
## 
## Model:
## CPUEn ~ depth + factor(id) + factor(survey)
##                Df    AIC     LRT   Pr(Chi)    
## <none>            400.21                      
## depth           1 398.34  0.1247 0.7239712    
## factor(id)      6 410.96 22.7474 0.0008856 ***
## factor(survey)  2 404.46  8.2476 0.0161830 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## ******************************************************************
##   Summary of the Randomised Quantile Residuals
##                            mean   =  0.00369002 
##                        variance   =  0.9940998 
##                coef. of skewness  =  0.6099116 
##                coef. of kurtosis  =  5.493112 
## Filliben correlation coefficient  =  0.9821562 
## ******************************************************************

## ******************************************************************
## Family:  c("ZAGA", "Zero Adjusted GA") 
## 
## Call:  gamlss(formula = CPUEw ~ depth + factor(id) + factor(survey),  
##     nu.formula = ~depth + factor(id) + factor(survey),  
##     family = ZAGA, data = c22) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -1.284504   0.235061  -5.465 6.62e-08 ***
## depth                 -0.001742   0.003227  -0.540  0.58946    
## factor(id)2            0.265502   0.200255   1.326  0.18537    
## factor(id)3            0.147263   0.250991   0.587  0.55759    
## factor(id)4           -0.440717   0.315634  -1.396  0.16310    
## factor(id)5           -0.195117   0.243270  -0.802  0.42281    
## factor(id)6           -0.015500   0.207863  -0.075  0.94058    
## factor(id)7           -0.057084   0.233182  -0.245  0.80669    
## factor(survey)2013002  0.031849   0.131891   0.241  0.80926    
## factor(survey)2013005 -0.498068   0.168559  -2.955  0.00324 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003268   0.035115   0.093    0.926
## 
## ------------------------------------------------------------------
## Nu link function:  logit 
## Nu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.134996   0.306882  -0.440    0.660    
## depth                 -0.003494   0.004343  -0.805    0.421    
## factor(id)2            0.107987   0.280948   0.384    0.701    
## factor(id)3            0.314591   0.346826   0.907    0.365    
## factor(id)4            0.023579   0.429432   0.055    0.956    
## factor(id)5            0.389988   0.327546   1.191    0.234    
## factor(id)6           -0.471680   0.307445  -1.534    0.125    
## factor(id)7            0.323361   0.321910   1.005    0.316    
## factor(survey)2013002  0.024872   0.188524   0.132    0.895    
## factor(survey)2013005  0.996838   0.213314   4.673 3.61e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  670 
## Degrees of Freedom for the fit:  21
##       Residual Deg. of Freedom:  649 
##                       at cycle:  2 
##  
## Global Deviance:     640.1004 
##             AIC:     682.1004 
##             SBC:     776.7532 
## ******************************************************************

## Single term deletions for
## mu
## 
## Model:
## CPUEw ~ depth + factor(id) + factor(survey)
##                Df    AIC     LRT  Pr(Chi)   
## <none>            682.10                    
## depth           1 680.39  0.2879 0.591551   
## factor(id)      6 680.18 10.0751 0.121526   
## factor(survey)  2 689.27 11.1739 0.003747 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## ******************************************************************
##   Summary of the Randomised Quantile Residuals
##                            mean   =  0.01402976 
##                        variance   =  0.957426 
##                coef. of skewness  =  0.2014206 
##                coef. of kurtosis  =  3.04717 
## Filliben correlation coefficient  =  0.9979663 
## ******************************************************************

Trap CPUE GAM models The CPUEn models had better fit than the CPUEw models, and models with nu-function specified had better fit than those without.

The overall bestfitting model was mod555 of CPUEn with survey as an ordniary factor both area and survey were significant variable (when excluding the smoothing function), but not depth. All survey and areas 1 & 5 were significant factors in the model

For the CPUEw model (mod333), only surveys was a significant varible, with only the Nov.2012 (intercept), and Nov.2013 survey being significant factors in the model.

Conclusion: difference in trap CPUE by surveys was significantly different, but not so between areas.

GAM model CPUE - gillnets only

## GAMLSS-RS iteration 1: Global Deviance = 161.0973 
## GAMLSS-RS iteration 2: Global Deviance = 161.0973

## GAMLSS-RS iteration 1: Global Deviance = 160.5285 
## GAMLSS-RS iteration 2: Global Deviance = 160.5285

## GAMLSS-RS iteration 1: Global Deviance = 184.9555 
## GAMLSS-RS iteration 2: Global Deviance = 184.9551

## GAMLSS-RS iteration 1: Global Deviance = 184.1489 
## GAMLSS-RS iteration 2: Global Deviance = 184.1487

## GAMLSS-RS iteration 1: Global Deviance = 136.3068 
## GAMLSS-RS iteration 2: Global Deviance = 136.3065

## GAMLSS-RS iteration 1: Global Deviance = 159.9272 
## GAMLSS-RS iteration 2: Global Deviance = 159.9267

##              df      AIC
## mod333 21.00000 178.3065
## mod22  10.00001 181.0973
## mod33  12.00000 184.5285
## mod555 21.00000 201.9267
## mod44  10.00001 204.9552
## mod55  12.00000 208.1487

## ******************************************************************
## Family:  c("ZAGA", "Zero Adjusted GA") 
## 
## Call:  gamlss(formula = CPUEw ~ depth + factor(id) + factor(survey),  
##     nu.formula = ~depth + factor(id) + factor(survey),  
##     family = ZAGA, data = c222) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.488227   0.645189   0.757   0.4533  
## depth                 -0.006344   0.004267  -1.487   0.1444  
## factor(id)2           -0.083590   0.552826  -0.151   0.8805  
## factor(id)3           -1.386996   0.718770  -1.930   0.0603 .
## factor(id)4           -0.390282   0.580326  -0.673   0.5048  
## factor(id)5           -0.291541   0.621620  -0.469   0.6414  
## factor(id)6           -1.611851   0.702743  -2.294   0.0268 *
## factor(id)7           -0.756164   0.644943  -1.172   0.2475  
## factor(survey)2013002 -0.010671   0.461668  -0.023   0.9817  
## factor(survey)2013005  0.245101   0.431467   0.568   0.5729  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01861    0.08747  -0.213    0.832
## 
## ------------------------------------------------------------------
## Nu link function:  logit 
## Nu Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -9.914184 272.559352  -0.036    0.971
## depth                  -0.008674   0.012620  -0.687    0.496
## factor(id)2            10.738405 272.558507   0.039    0.969
## factor(id)3            -1.835308 416.422793  -0.004    0.997
## factor(id)4            11.495711 272.558213   0.042    0.967
## factor(id)5             9.960912 272.558854   0.037    0.971
## factor(id)6            -1.789029 399.478786  -0.004    0.996
## factor(id)7             8.160657 272.561501   0.030    0.976
## factor(survey)2013002  -1.242747   1.071254  -1.160    0.252
## factor(survey)2013005 -13.337185 138.032693  -0.097    0.923
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  64 
## Degrees of Freedom for the fit:  21
##       Residual Deg. of Freedom:  43 
##                       at cycle:  2 
##  
## Global Deviance:     136.3065 
##             AIC:     178.3065 
##             SBC:     223.643 
## ******************************************************************

## Single term deletions for
## mu
## 
## Model:
## CPUEw ~ depth + factor(id) + factor(survey)
##                Df    AIC    LRT Pr(Chi)
## <none>            178.31               
## depth           1 177.89 1.5827  0.2084
## factor(id)      6 175.33 9.0219  0.1723
## factor(survey)  2 174.88 0.5688  0.7525

## ******************************************************************
##   Summary of the Randomised Quantile Residuals
##                            mean   =  0.002960327 
##                        variance   =  1.046671 
##                coef. of skewness  =  -0.1705965 
##                coef. of kurtosis  =  2.149268 
## Filliben correlation coefficient  =  0.9903634 
## ******************************************************************

## ******************************************************************
## Family:  c("ZAGA", "Zero Adjusted GA") 
## 
## Call:  gamlss(formula = CPUEn ~ depth + factor(id) + factor(survey),  
##     nu.formula = ~depth + factor(id) + factor(survey),  
##     family = ZAGA, data = c222) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           -0.209808   0.640161  -0.328    0.745    
## depth                 -0.016392   0.003818  -4.293 9.85e-05 ***
## factor(id)2            0.763394   0.591571   1.290    0.204    
## factor(id)3           -0.610027   0.766685  -0.796    0.431    
## factor(id)4            0.673012   0.578518   1.163    0.251    
## factor(id)5            0.881939   0.619440   1.424    0.162    
## factor(id)6            0.183003   0.714614   0.256    0.799    
## factor(id)7            0.553867   0.634909   0.872    0.388    
## factor(survey)2013002  0.110705   0.472581   0.234    0.816    
## factor(survey)2013005  0.353101   0.425734   0.829    0.411    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01585    0.08694   0.182    0.856
## 
## ------------------------------------------------------------------
## Nu link function:  logit 
## Nu Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)            -9.914184 272.883567  -0.036    0.971
## depth                  -0.008674   0.012620  -0.687    0.496
## factor(id)2            10.738405 272.882720   0.039    0.969
## factor(id)3            -1.835308 416.855152  -0.004    0.997
## factor(id)4            11.495711 272.882428   0.042    0.967
## factor(id)5             9.960912 272.883068   0.037    0.971
## factor(id)6            -1.789029 399.700064  -0.004    0.996
## factor(id)7             8.160657 272.885715   0.030    0.976
## factor(survey)2013002  -1.242747   1.071254  -1.160    0.252
## factor(survey)2013005 -13.337185 138.032692  -0.097    0.923
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  64 
## Degrees of Freedom for the fit:  21
##       Residual Deg. of Freedom:  43 
##                       at cycle:  2 
##  
## Global Deviance:     159.9267 
##             AIC:     201.9267 
##             SBC:     247.2632 
## ******************************************************************

## Single term deletions for
## mu
## 
## Model:
## CPUEn ~ depth + factor(id) + factor(survey)
##                Df    AIC    LRT  Pr(Chi)   
## <none>            201.93                   
## depth           1 209.09 9.1580 0.002476 **
## factor(id)      6 196.67 6.7395 0.345616   
## factor(survey)  2 198.73 0.8065 0.668160   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## ******************************************************************
##   Summary of the Randomised Quantile Residuals
##                            mean   =  0.01991237 
##                        variance   =  0.9986423 
##                coef. of skewness  =  0.01118196 
##                coef. of kurtosis  =  2.542639 
## Filliben correlation coefficient  =  0.9922952 
## ******************************************************************

Gillnet CPUE GAM models The gillnet GAM models had much lower AICs than the trap models, indicating a better overall model fit for gillnet data than for trap data.

All CPUEw models had lower AIC than the CPUEn model (in contrast to the traps GAM models)

The best fitting model was the CPUEw model with survey as an ordinary factor and the nu.function specified (mod333). Only area 3 & 6 were significant factors in the model, and in contrast to the traps surveys were not a significant factors.

Conclusion: there were no significant differences in CPUE (w) for gillnets between surveys, and only significant differences involving areas 3 and 6.

---

Bottom depth traps and gillnets

## 
##  Welch Two Sample t-test
## 
## data:  subset(cpue.st, gear == "TB")$depth and (subset(cpue.st, gear == "GN")$depth)
## t = 3.5683, df = 67.859, p-value = 0.0006648
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   6.034582 21.347788
## sample estimates:
## mean of x mean of y 
##  31.73806  18.04688

Depth was, however, not a significant factor in any of the four models developed for the trap CPUE, by weight or numbers. The depth distribution of bottom depth at the trap and gillnet stations (Gillnet fishing depth was at the surface, while trap fishing depth = bottom depth) were significantly different (t-test, p<0.05, df=67.9 - see also frequncy plot above). Therefore, the models including both gear types led to a wider span and a more diverse distribution of the depths than for the traps-only data set.

---

Combine trap and gillnet duration + CPUE plots

---

CPUE by gear type - by family and trophic group (gearplot)

Only for gillnets and traps and stations with catches (excluding no-catch)

TRAP CATCHES by depth bins (0-30m and >30m)

Split CPUEw by depth ranges (0-30m and >30m) for the trap catches. Gillnets were all taken at the surface so belong in the upper depth range.

Multivariate analysis of catch composition

PCA analysis was carried out to explore the variability in the catch data (CPUE-by-weight) in relation to: - survey - gear - area - Trophic group

PCA was carried out on average catches (CPUE) per survey and area (needed to create a M*N matrix - cannot have uneven number of observations per group).

PCA Gillnets and traps, no depth bins

##                     PC1         PC2         PC3          PC4           PC5 gear
## GN_2012901_2 -0.1385169 -0.08034762 -0.08267280  0.022102618  5.431281e-05   GN
## GN_2012901_3 -0.1415117 -0.10512615 -0.08175626 -0.011103180 -8.979356e-05   GN
## GN_2012901_5 -0.1461118  0.12919315 -0.07520536 -0.007790960  3.048672e-04   GN
## GN_2012901_6 -0.1453167  0.08869074 -0.07633769 -0.008363482  2.366495e-04   GN
## GN_2012901_7 -0.1404506 -0.15917516 -0.08326732 -0.011867189 -1.808276e-04   GN
## GN_2013002_1 -0.1249389  0.14835990 -0.07432751 -0.009791310  3.346150e-04   GN
##               survey area
## GN_2012901_2 2012901    2
## GN_2012901_3 2012901    3
## GN_2012901_5 2012901    5
## GN_2012901_6 2012901    6
## GN_2012901_7 2012901    7
## GN_2013002_1 2013002    1

##                   PC1          PC2         PC3          PC4           PC5
## Carni.   0.0196184927 -0.999315870 -0.02793805 -0.014125826 -0.0016831338
## Herb.    0.1054558856 -0.010605169 -0.04894918  0.993157135  0.0030771125
## Plankt.  0.9941742176  0.020538052  0.01635847 -0.104538066 -0.0000823814
## Invert. -0.0105679342 -0.028801239  0.99820062  0.050050201 -0.0122191691
## Coral.  -0.0003387364 -0.001999746  0.01230312 -0.002477072  0.9999191885
##         Trophic_Group
## Carni.         Carni.
## Herb.           Herb.
## Plankt.       Plankt.
## Invert.       Invert.
## Coral.         Coral.

PCA of TRAP catches split in depth categories (<30m and >30m)

(gillnets were only at the surface)

##                            PC1          PC2          PC3           PC4
## TB_2012901_1_<30m  0.002617937  0.011362987 -0.096517255 -0.0010775816
## TB_2012901_1_>30m -0.057667184  0.034936698 -0.007360843 -0.0016139402
## TB_2012901_2_<30m  0.295125463 -0.066927684 -0.044668108  0.0026241585
## TB_2012901_2_>30m  0.102648502 -0.006624161  0.038833922  0.0007180965
## TB_2012901_3_<30m  0.139611391 -0.023114102 -0.042842006  0.0007227938
## TB_2012901_3_>30m  0.122198277 -0.028139788 -0.175948243  0.0002073881
##                             PC5  survey area depth
## TB_2012901_1_<30m  0.0007591324 2012901    1  <30m
## TB_2012901_1_>30m  0.0004591729 2012901    1  >30m
## TB_2012901_2_<30m  0.0023524390 2012901    2  <30m
## TB_2012901_2_>30m -0.0194403608 2012901    2  >30m
## TB_2012901_3_<30m  0.0015137099 2012901    3  <30m
## TB_2012901_3_>30m  0.0013818531 2012901    3  >30m

##                  PC1           PC2           PC3         PC4          PC5
## Carni.   0.708269047 -0.1474988955  0.6902738068 0.010244759  0.004018719
## Invert.  0.652272628 -0.2368360468 -0.7199983925 0.006358460  0.003315134
## Coral.   0.005160458 -0.0002843214  0.0002769255 0.012714451 -0.999905773
## Plankt. -0.269723456 -0.9602738107  0.0715348915 0.002253698 -0.001070507
## Herb.   -0.010862927  0.0051855946 -0.0026587615 0.999843927  0.012655391
##         Trophic_Group
## Carni.         Carni.
## Invert.       Invert.
## Coral.         Coral.
## Plankt.       Plankt.
## Herb.           Herb.

Combine PCA plots

In Figure 4 panels A and B clearly show how the catch composition differed between traps and gillnets, both in relation to trophic groups and main fish families caught. Carinvores were caught in similar quantities by both the traps and gillnets, coralivores only in traps (but in very low quantities) invertivores mainly in traps, planktivores and herbivores mainly in gillnets.

Catch composition by families (Figure 4 panel A and B) showed clear differences in the species selectivity of the gear types.

Catch composition of traps showed some differences by depth range (<30m or > 30m), for the plantivores trophic group and Acunthuridae and Carangidae families that were only caught at shallow depths, while no trophic groups or families were uniquely caught at depths greater than 30m.

PCA of catch weight and numbers (CPUEw and CPUEw) by trophic group gave similar results and showed how trap and gillnet catches differed. The following analysis therefore focused solely on PCA by CPUEw.

In the PCA analysis of both gillnet and traps (Figure 4, panel C and D). The first two principal components explained 88% of the variation. A large gillnet catch of Planktivores (56.69 kg gillnet catch of Naso hexacanthus in area 2 during the Nov. 2013 survey) drove the variability along PC1. PC2 was driven by high catches of carnivores during the November 2013 survey d. Catches of coralivores, invertivores and herbivores contributed little to the variability in either of the two first PCs (but these were caught in low quantities in just a few surveys and areas).

The PCA analysis of the trap catches (Figure 4, panel E and F) the first two PCs explained 67.9% of the variability with PC1 and PC2 contributing fairly equally (35.9% and 32% respectively). Catches of carnivores and invertivores drove the variability along PC1 while a 10.14kg catch of planktivores (Naso hexacanthus) in area 5 in May 2013 in shallow waters drove the variability along PC2. Apart from the May.2013 point no areas showed particular differences between deep and shallow areas.

Trap catches were dominated by herbivores, gillnet catches by carnivores, while gillnet catches in area 2 during the Nov. 2013 survey had very high catches of planktivores compared to other gears, surveys and areas. Trap catches did not show marked difference by depth category, except for Area 5 during the May 2013 survey where there were (relatively) large catches of planktivores.

However, this is still descriptive. Need to evaluate the differences statistically. Will use GAM model.
(This also allows to use data on station level, not just average catches per area/survey needed to construct a matrix necessary for PCA)

---

Catch composition - GAM Models CPUEw

Expand on the PCA analysis and descriptive plots using GAM models. Compartive, evaluating the same variables as in the PCA analysis: - Survey - Area - Depth Category (shallow / deep) - Trophic group

Separate models for Gillnets and Traps,adding depth category as factor variable.

Trap catch comp GAMs

## GAMLSS-RS iteration 1: Global Deviance = -113.6846 
## GAMLSS-RS iteration 2: Global Deviance = -113.6846

## minimum GAIC(k= 2 ) family: GIG 
## minimum GAIC(k= 3.84 ) family: IG 
## minimum GAIC(k= 5.83 ) family: IG

## GAIG with k= 5.83

##         IG        GIG        LNO      LOGNO     LOGNO2         GG       BCTo 
##  -538.4687  -534.3554  -530.4779  -530.4779  -530.4779  -525.5563  -519.6675 
##        GB2     IGAMMA   PARETO2o    PARETO2         GP        EXP       WEI3 
##  -507.5812  -505.0091  -492.4760  -492.4757  -492.4757  -489.1199  -485.4980 
##        WEI       WEI2         GA      BCPEo      BCCGo       BCCG     exGAUS 
##  -485.4980  -485.4979  -483.3363   812.8877 18578.5953         NA         NA 
##        BCT       BCPE 
##         NA         NA

## GAIG with k= 5.83 
## GAMLSS-RS iteration 1: Global Deviance = -608.4287 
## GAMLSS-RS iteration 2: Global Deviance = -608.4288

## GAIG with k= 5.83 
## GAMLSS-RS iteration 1: Global Deviance = -608.4687 
## GAMLSS-RS iteration 2: Global Deviance = -608.4688

## GAIG with k= 5.83 
## GAMLSS-RS iteration 1: Global Deviance = -608.4287 
## GAMLSS-RS iteration 2: Global Deviance = -608.4288

## GAIG with k= 5.83 
## GAMLSS-RS iteration 1: Global Deviance = -608.1104 
## GAMLSS-RS iteration 2: Global Deviance = -608.1105

##             df       AIC
## mod10 12.00000 -584.4288
## mod7  12.00002 -584.4287
## mod12 12.00000 -584.1105
## mod8  13.00000 -582.4688

## GAIG with k= 5.83 
## ******************************************************************
## Family:  c("IG", "Inverse Gaussian") 
## 
## Call:  gamlss(formula = CPUEw ~ factor(Drng) + factor(id) +  
##     factor(TGShort), family = names(getOrder(t1, 3)[1]),  
##     data = na.omit(subset(c43, gear == "TB" & CPUEw >          0))) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            -1.63095    0.26401  -6.178 1.93e-09 ***
## factor(Drng)>30m       -0.11392    0.17945  -0.635   0.5260    
## factor(id)2             0.15922    0.30543   0.521   0.6025    
## factor(id)3             0.40006    0.40240   0.994   0.3209    
## factor(id)4            -0.85216    0.44562  -1.912   0.0567 .  
## factor(id)5            -0.29039    0.33324  -0.871   0.3842    
## factor(id)6            -0.03484    0.29693  -0.117   0.9067    
## factor(id)7            -0.36246    0.30545  -1.187   0.2362    
## factor(TGShort)Coral.  -2.57419    0.35401  -7.272 2.64e-12 ***
## factor(TGShort)Invert. -0.27942    0.17003  -1.643   0.1013    
## factor(TGShort)Plankt.  0.79255    1.41869   0.559   0.5768    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.2877     0.0384   33.53   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  339 
## Degrees of Freedom for the fit:  12
##       Residual Deg. of Freedom:  327 
##                       at cycle:  2 
##  
## Global Deviance:     -608.4288 
##             AIC:     -584.4288 
##             SBC:     -538.5168 
## ******************************************************************

## GAIG with k= 5.83 
## GAIG with k= 5.83 
## GAIG with k= 5.83

## Single term deletions for
## mu
## 
## Model:
## CPUEw ~ factor(Drng) + factor(id) + factor(TGShort)
##                 Df     AIC     LRT  Pr(Chi)   
## <none>             -584.43                    
## factor(Drng)     1 -586.03  0.3957 0.529316   
## factor(id)       6 -587.09  9.3419 0.155245   
## factor(TGShort)  3 -578.95 11.4739 0.009421 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Gillnet catch comp GAMs

Excluding depth as a factor

## GAMLSS-RS iteration 1: Global Deviance = 14.5504 
## GAMLSS-RS iteration 2: Global Deviance = 14.5504

## minimum GAIC(k= 2 ) family: LNO 
## minimum GAIC(k= 3.84 ) family: EXP 
## minimum GAIC(k= 4.39 ) family: EXP

## GAIG with k= 4.39

##        EXP        LNO      LOGNO     LOGNO2    PARETO2         GP       WEI3 
##  -90.41875  -89.61889  -89.61889  -89.61889  -89.12484  -89.12484  -87.04005 
##        WEI       WEI2         GG         GA        GIG        GB2       BCTo 
##  -87.04003  -87.03958  -86.56286  -86.42946  -84.14779  -83.26668  -82.94668 
##        BCT         IG     IGAMMA   PARETO2o      BCPEo      BCCGo       BCCG 
##  -81.76755  -79.32936  -74.70143  -12.60698   10.48574 4411.12730         NA 
##     exGAUS       BCPE 
##         NA         NA

## GAIG with k= 4.39 
## GAMLSS-RS iteration 1: Global Deviance = -134.3253 
## GAMLSS-RS iteration 2: Global Deviance = -134.3253

## GAIG with k= 4.39 
## GAMLSS-RS iteration 1: Global Deviance = -134.3307 
## GAMLSS-RS iteration 2: Global Deviance = -134.3307

## GAIG with k= 4.39 
## GAMLSS-RS iteration 1: Global Deviance = -134.3253 
## GAMLSS-RS iteration 2: Global Deviance = -134.3253

## GAIG with k= 4.39 
## GAMLSS-RS iteration 1: Global Deviance = -106.0583 
## GAMLSS-RS iteration 2: Global Deviance = -106.0583

##              df        AIC
## mod10a 10.00000 -114.32531
## mod7a  10.00149 -114.32232
## mod8a  11.00000 -112.33073
## mod12a  5.00000  -96.05828

## GAIG with k= 4.39 
## ******************************************************************
## Family:  c("EXP", "Exponential") 
## 
## Call:  gamlss(formula = CPUEw ~ factor(id) + random(factor(survey)) +  
##     factor(TGShort), family = names(getOrder(t1, 3)[1]),  
##     data = na.omit(subset(c43, gear == "GN" & CPUEw >          0))) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             -3.2144     0.5723  -5.617 3.54e-07 ***
## factor(id)2              1.6471     0.5936   2.775 0.007058 ** 
## factor(id)3              3.6041     1.1609   3.105 0.002736 ** 
## factor(id)4              2.4232     0.6727   3.602 0.000581 ***
## factor(id)5              1.6098     0.6093   2.642 0.010125 *  
## factor(id)6              0.9530     0.6331   1.505 0.136650    
## factor(id)7              2.4219     0.6623   3.657 0.000486 ***
## factor(TGShort)Herb.    -1.3321     0.3884  -3.429 0.001011 ** 
## factor(TGShort)Invert.  -0.8166     0.4456  -1.832 0.071081 .  
## factor(TGShort)Plankt.  -0.5581     0.5908  -0.945 0.347993    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## NOTE: Additive smoothing terms exist in the formulas: 
##  i) Std. Error for smoothers are for the linear effect only. 
## ii) Std. Error for the linear terms maybe are not accurate. 
## ------------------------------------------------------------------
## No. of observations in the fit:  81 
## Degrees of Freedom for the fit:  10.00149
##       Residual Deg. of Freedom:  70.99851 
##                       at cycle:  2 
##  
## Global Deviance:     -134.3253 
##             AIC:     -114.3223 
##             SBC:     -90.37426 
## ******************************************************************

## GAIG with k= 4.39 
## GAIG with k= 4.39 
## GAIG with k= 4.39

## Single term deletions for
## mu
## 
## Model:
## CPUEw ~ factor(id) + random(factor(survey)) + factor(TGShort)
##                               Df      AIC    LRT   Pr(Chi)    
## <none>                           -114.322                     
## factor(id)             6.0014820  -97.291 29.034 6.002e-05 ***
## random(factor(survey)) 0.0014933 -114.325  0.000  0.009631 ** 
## factor(TGShort)        3.0014485 -108.948 11.377  0.009866 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

####Hyperstability trap catches - max number of fish per trap Asymptotic histogram - no indication of reaching a plateau, only low probability of catching many fish.

Test differences in CPUE By Species in trap catches, by range of traits

Use GAM model and evaluate how CPUE by weight (CPUEw) was affected by the traits :

• trophic group
• trophic level (linked to trophic group)
• Place in water column
• Diel activity
• Habitat
• Gregariousness
• MaxLength

GAM CPUEw - all traits

(excluding depth, area and survey)

## GAMLSS-RS iteration 1: Global Deviance = -533.6391 
## GAMLSS-RS iteration 2: Global Deviance = -533.6391

## minimum GAIC(k= 2 ) family: BCPEo 
## minimum GAIC(k= 3.84 ) family: BCPEo 
## minimum GAIC(k= 5.8 ) family: BCPEo

## GAIG with k= 5.8

##      BCPEo        BCT       BCTo        GB2         IG      BCCGo     IGAMMA 
## -1320.2608 -1222.8654 -1222.7411 -1178.6756 -1165.0550 -1161.7199 -1158.3079 
##         GG        GIG     LOGNO2      LOGNO        LNO         GA       WEI3 
## -1156.6036 -1152.5063 -1126.3848 -1126.3848 -1126.3848 -1071.0676 -1020.6140 
##        WEI       WEI2        EXP         GP    PARETO2   PARETO2o       BCCG 
## -1020.6140 -1020.3713  -890.8462  -860.1424  -860.1424         NA         NA 
##     exGAUS       BCPE 
##         NA         NA

## GAIG with k= 5.8 
## GAMLSS-RS iteration 1: Global Deviance = -963.7931 
## GAMLSS-RS iteration 2: Global Deviance = -1126.689 
## GAMLSS-RS iteration 3: Global Deviance = -1245.823 
## GAMLSS-RS iteration 4: Global Deviance = -1357.345 
## GAMLSS-RS iteration 5: Global Deviance = -1383.994 
## GAMLSS-RS iteration 6: Global Deviance = -1395.602 
## GAMLSS-RS iteration 7: Global Deviance = -1403.267 
## GAMLSS-RS iteration 8: Global Deviance = -1408.302 
## GAMLSS-RS iteration 9: Global Deviance = -1411.131 
## GAMLSS-RS iteration 10: Global Deviance = -1413.249 
## GAMLSS-RS iteration 11: Global Deviance = -1414.883 
## GAMLSS-RS iteration 12: Global Deviance = -1416.246 
## GAMLSS-RS iteration 13: Global Deviance = -1417.419 
## GAMLSS-RS iteration 14: Global Deviance = -1418.103 
## GAMLSS-RS iteration 15: Global Deviance = -1417.253 
## GAMLSS-RS iteration 16: Global Deviance = -1418.231 
## GAMLSS-RS iteration 17: Global Deviance = -1414.322 
## GAMLSS-RS iteration 18: Global Deviance = -1421.598 
## GAMLSS-RS iteration 19: Global Deviance = -1418.595 
## GAMLSS-RS iteration 20: Global Deviance = -1418.861

## GAIG with k= 5.8 
## ******************************************************************
## Family:  c("BCPEo", "Box-Cox Power Exponential-orig.") 
## 
## Call:  gamlss(formula = CPUEn ~ TrophicLevel + factor(TGShort) +  
##     factor(WaterCol) + factor(DielActivity) + factor(Habitat) +  
##     factor(Gregariousness) + MaxLength, family = names(getOrder(t1,  
##     3)[1]), data = na.omit(ct)) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                                             Estimate Std. Error   t value
## (Intercept)                               -2.6567135  0.0023312 -1139.647
## TrophicLevel                              -0.0545254  0.0002972  -183.479
## factor(TGShort)Coral.                     -0.2104276  0.0016784  -125.375
## factor(TGShort)Invert.                    -0.0363964  0.0012421   -29.302
## factor(TGShort)Plankt.                     2.8405777  0.0028872   983.855
## factor(WaterCol)Demersal                   0.0817585  0.0020562    39.763
## factor(WaterCol)pelagic non-site attached  0.0896340  0.0027945    32.075
## factor(WaterCol)pelagic site attached     -2.4831072  0.0029956  -828.924
## factor(DielActivity)Night                 -0.0207855  0.0018257   -11.385
## factor(Habitat)Coral                       2.5952503  0.0029202   888.729
## factor(Habitat)sand                        0.3949726  0.0019661   200.894
## factor(Gregariousness)2                   -0.0078488  0.0011942    -6.572
## factor(Gregariousness)3                    0.0050428  0.0021788     2.314
## MaxLength                                  0.0004446  0.0001008     4.411
##                                           Pr(>|t|)    
## (Intercept)                                < 2e-16 ***
## TrophicLevel                               < 2e-16 ***
## factor(TGShort)Coral.                      < 2e-16 ***
## factor(TGShort)Invert.                     < 2e-16 ***
## factor(TGShort)Plankt.                     < 2e-16 ***
## factor(WaterCol)Demersal                   < 2e-16 ***
## factor(WaterCol)pelagic non-site attached  < 2e-16 ***
## factor(WaterCol)pelagic site attached      < 2e-16 ***
## factor(DielActivity)Night                  < 2e-16 ***
## factor(Habitat)Coral                       < 2e-16 ***
## factor(Habitat)sand                        < 2e-16 ***
## factor(Gregariousness)2                   2.08e-10 ***
## factor(Gregariousness)3                     0.0213 *  
## MaxLength                                 1.42e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.6509     0.3242   2.007   0.0456 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Nu link function:  identity 
## Nu Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.8710     0.1941  -4.487 1.02e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Tau link function:  log 
## Tau Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.3799     0.1233  -11.19   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  329 
## Degrees of Freedom for the fit:  17
##       Residual Deg. of Freedom:  312 
##                       at cycle:  20 
##  
## Global Deviance:     -1418.861 
##             AIC:     -1384.861 
##             SBC:     -1320.328 
## ******************************************************************

## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8

## Single term deletions for
## mu
## 
## Model:
## CPUEn ~ TrophicLevel + factor(TGShort) + factor(WaterCol) + factor(DielActivity) + 
##     factor(Habitat) + factor(Gregariousness) + MaxLength
##                        Df     AIC     LRT   Pr(Chi)    
## <none>                    -1384.9                      
## TrophicLevel            1 -1385.7  1.2152 0.2702995    
## factor(TGShort)         3 -1361.5 29.3473 1.893e-06 ***
## factor(WaterCol)        3 -1368.1 22.7719 4.505e-05 ***
## factor(DielActivity)    1 -1380.0  6.8626 0.0088020 ** 
## factor(Habitat)         2 -1362.3 26.5737 1.697e-06 ***
## factor(Gregariousness)  2 -1374.1 14.7961 0.0006125 ***
## MaxLength               1 -1385.2  1.6076 0.2048317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## ******************************************************************
##        Summary of the Quantile Residuals
##                            mean   =  0.21142 
##                        variance   =  1.052263 
##                coef. of skewness  =  0.1719285 
##                coef. of kurtosis  =  2.067135 
## Filliben correlation coefficient  =  0.9855425 
## ******************************************************************

Evaluting catches of fish in relation to their traits. Should be evaluated by numbers, for single large fish not to dominate the results. The model based on CPUE by numbers (mod12, where surveys, area and depth are excluded) had lower AIC than models with numbers and fishing hours as a factor. Also, including survey as an ordinary factor led to lower AIC than including it as a random factor.

Model (BCPOe Box-Cox Power Exponential-orig. distribution, AIC: -1384.861, df:17) results show that numbers of fish caught (CPUE by numbers) was significantly affected by:

• all traits, except MaxLength and WaterColumn (demersal)

CPUE by trophic group by area & by survey (year)

Presents the CPUE pr hr, for each management area and survey, standardized by the number of traps set in each area X survey combination.

Trap catches were dominated by carnivores, occuring in all area X survey combinations, followed by invertivores who also occurred everywhere, albeight in lower densities than the carnivores.

Cathces of planktivores and invertivores varied substantially between areas and surveys, in contrast to carnivores who were caught in all surveys and areas. This explains why invertivores and planktivores were signifant factors on the catches (GAM model of CPUEn with traits), while carnivores were not.

Area 2, 5 and 6 seem to have the highest diversity of trophic groups, but this has to be calculated as functional diversity, using the FD package (or similar).

## quartz_off_screen 
##                 2

Trophic group CPUE by depth

A modified version of Figure 6 in Feb 2019 MS.

Basically a more complex version of the previous plot. Adds the depth dimension to the plot, and the surveys as colours.

Shows uniform catch rates of carnivores and invertivores from 0-50m.

All surveys seem to overlap, so no difference in trophich group depth dependent catch rates between the surveys.

## quartz_off_screen 
##                 2

Trophic level in Trap catches

## quartz_off_screen 
##                 2

GAM model of trophic level of catches

## GAMLSS-RS iteration 1: Global Deviance = 186.3385 
## GAMLSS-RS iteration 2: Global Deviance = 186.3385

## minimum GAIC(k= 2 ) family: BCPEo 
## minimum GAIC(k= 3.84 ) family: BCPEo 
## minimum GAIC(k= 5.85 ) family: BCPEo

## GAIG with k= 5.85

##     BCPEo      BCPE       WEI      WEI3      WEI2        GG       GB2      BCTo 
##  190.2248  190.3103  218.5686  218.5686  218.5696  219.0720  225.8835  229.9747 
##       BCT     BCCGo      BCCG    exGAUS        GA     LOGNO       LNO    LOGNO2 
##  230.0487  236.0337  236.0926  250.7759  253.4726  258.1262  258.1262  258.1262 
##        IG       GIG    IGAMMA       EXP  PARETO2o        GP   PARETO2 
##  258.5443  259.6889  263.1506 1684.4977 1695.0055 1722.6221 1722.6221

## GAIG with k= 5.8

## [1] "BCPEo"

## GAIG with k= 5.8 
## GAMLSS-RS iteration 1: Global Deviance = 131.925 
## GAMLSS-RS iteration 2: Global Deviance = 121.8234 
## GAMLSS-RS iteration 3: Global Deviance = 120.5852 
## GAMLSS-RS iteration 4: Global Deviance = 120.2275 
## GAMLSS-RS iteration 5: Global Deviance = 120.1006 
## GAMLSS-RS iteration 6: Global Deviance = 120.0508 
## GAMLSS-RS iteration 7: Global Deviance = 120.0366 
## GAMLSS-RS iteration 8: Global Deviance = 120.0287 
## GAMLSS-RS iteration 9: Global Deviance = 120.0266 
## GAMLSS-RS iteration 10: Global Deviance = 120.0251 
## GAMLSS-RS iteration 11: Global Deviance = 120.0248

## GAIG with k= 5.8 
## ******************************************************************
## Family:  c("BCPEo", "Box-Cox Power Exponential-orig.") 
## 
## Call:  gamlss(formula = TrophicLevel ~ depth + factor(id) +  
##     factor(survey), family = names(getOrder(t1, 3)[1]),  
##     data = na.omit(ct)) 
## 
## Fitting method: RS() 
## 
## ------------------------------------------------------------------
## Mu link function:  log
## Mu Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.3630905  0.0129179 105.519   <2e-16 ***
## depth                 -0.0002265  0.0001722  -1.315   0.1893    
## factor(id)2            0.0096046  0.0110097   0.872   0.3836    
## factor(id)3            0.0316831  0.0139916   2.264   0.0242 *  
## factor(id)4            0.0002802  0.0133868   0.021   0.9833    
## factor(id)5            0.0057113  0.0132689   0.430   0.6672    
## factor(id)6           -0.0090797  0.0111079  -0.817   0.4143    
## factor(id)7            0.0021158  0.0120088   0.176   0.8603    
## factor(survey)2013002 -0.0001105  0.0041665  -0.027   0.9789    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Sigma link function:  log
## Sigma Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.57961    0.03129  -82.43   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Nu link function:  identity 
## Nu Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.2984     0.4915    6.71 8.34e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## Tau link function:  log 
## Tau Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.5043     0.1077   13.96   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## ------------------------------------------------------------------
## No. of observations in the fit:  346 
## Degrees of Freedom for the fit:  12
##       Residual Deg. of Freedom:  334 
##                       at cycle:  11 
##  
## Global Deviance:     120.0248 
##             AIC:     144.0248 
##             SBC:     190.1821 
## ******************************************************************

## GAIG with k= 5.8 
## GAIG with k= 5.8 
## GAIG with k= 5.8

## Single term deletions for
## mu
## 
## Model:
## TrophicLevel ~ depth + factor(id) + factor(survey)
##                Df    AIC     LRT Pr(Chi)  
## <none>            144.03                  
## depth           1 143.68  1.6516 0.19874  
## factor(id)      6 144.89 12.8634 0.04526 *
## factor(survey)  1 142.03  0.0005 0.98153  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The GAM Model (Box-Cox Power Exponential, AIC: 144.0248, df: 12) of trophic level by depth, area and surveys only showed area 3 and the intercept to be significant factors affecting the Trophic level of catches meaning that trophic level was similar in all areas except area 3 (and 1)

Maxlength in catches

## quartz_off_screen 
##                 2

Place in water column

## quartz_off_screen 
##                 2

Diel activity

## quartz_off_screen 
##                 2

Gregariousness

## quartz_off_screen 
##                 2

---

Biodiversity (species densities, trophic diversity etc)

Species accumulation curves

##    area spc deep.acc$spc gill.acc$spc
## 1   All  49           43           93
## 2 Area1   9           13           14
## 3 Area2  24           27           38
## 4 Area3  17            9            5
## 5 Area4   4            8           22
## 6 Area5  18            2           35
## 7 Area6  19           15           13
## 8 Area7  16           13           37

---

Functional diversity

UPDATE March 2021. Including both traps and gillnet data in analysis.

We did not split the FD analysis by depth (<> 30m) as this would bias the shallow bin because that would include both gillnets and traps while the deep bin only included trap catches.

We used the same approach as Stuart-Smith et al. to exclude very few observations of a single species and excluded all species observations occuring in 2 or fewer stations (traps).

To avoid dbFD crashing ‘m’ was set in the ‘dbFD’ call (the number of PoCA axes kept as traits to do the FRic analysis). Several levels of ‘m’ were tested, and this could probably be tested further (to higher levels of ‘m’)

Traps & Gillnets, Results for n_spec > 2 m=10

Results based on the output from the dbFD analysis:

Overall Fric (R2)= 0.533 (compared to 0.62 for traps only data)

## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5331195

	Area	FEve	FDiv	FDis	RaoQ
A1	A1	0.796	0.889	0.269	0.09
A2	A2	0.735	0.85	0.282	0.087
A3	A3	0.7	0.873	0.237	0.063
A4	A4	0.669	0.905	0.183	0.038
A5	A5	0.62	0.827	0.235	0.069
A6	A6	0.842	0.738	0.222	0.063
A7	A7	0.737	0.841	0.221	0.068

Rerunning the FD model excluding one trait at a time

Need to reduce ‘m’ to 9 to avoid crash:

Zero distance(s)Error in convhulln(tr.FRic, “FA”) : Received error code 2 from qhull. Qhull error: QH6114 qhull precision error: initial simplex is not convex. Distance=1.4e-16

Removing traits only improved the R2 by max 9.6% (by removing Maxlength). Therefore decided to keep all the traits in the model for Traps and Gillnets

## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 22 PCoA axes (out of 32 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5838667 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 23 PCoA axes (out of 33 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5458381 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5167443 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.4948348 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 27 PCoA axes (out of 37 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5634173 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5033913 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 28 PCoA axes (out of 38 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5619108

	All	MaxLength	Trophic.Level	Trophic.group	Water.column	Habitat	Gregariousness	diel
R2	0.533	0.584	0.546	0.517	0.495	0.563	0.503	0.562

	All	MaxLength	Trophic.Level	Trophic.group	Water.column	Habitat	Gregariousness	diel
A1	0.090	0.109	0.103	0.075	0.072	0.107	0.082	0.094
A2	0.087	0.109	0.099	0.066	0.065	0.107	0.076	0.105
A3	0.063	0.075	0.068	0.046	0.050	0.086	0.051	0.078
A4	0.038	0.045	0.043	0.047	0.034	0.030	0.029	0.047
A5	0.069	0.092	0.079	0.056	0.051	0.079	0.062	0.076
A6	0.063	0.075	0.078	0.054	0.050	0.082	0.056	0.065
A7	0.068	0.073	0.077	0.063	0.050	0.078	0.068	0.078

Trap only - RaosQ

For traps excluding traits had an even less effect than for the traps & gillnet analysis. The only removal of trait that increased R2 was for trophic level, but the increase was only 0.7%. Therefore the full model including all traits was used in the manuscript.

## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8260286

	Area	FEve	FDiv	FDis	RaoQ
A1	A1	0.86	0.841	0.257	0.076
A2	A2	0.828	0.892	0.23	0.066
A3	A3	0.794	0.908	0.227	0.069
A4	A4	0.82	0.84	0.225	0.059
A5	A5	0.787	0.885	0.22	0.063
A6	A6	0.886	0.873	0.267	0.084
A7	A7	0.685	0.854	0.238	0.062

## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 8 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8190764 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 5 PCoA axes (out of 14 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8316098 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.7674298 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8002454 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.80821 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.7906913 
## Species x species distance matrix was not Euclidean. Lingoes correction was applied. 
## FRic: Dimensionality reduction was required. The last 7 PCoA axes (out of 16 in total) were removed. 
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.8237468

	All	MaxLength	Trophic.Level	Trophic.group	Water.column	Habitat	Gregariousness	diel
R2	0.826	0.819	0.832	0.767	0.8	0.808	0.791	0.824

	All	MaxLength	Trophic.Level	Trophic.group	Water.column	Habitat	Gregariousness	diel
A1	0.076	0.095	0.078	0.066	0.066	0.100	0.059	0.087
A2	0.066	0.082	0.070	0.063	0.050	0.083	0.060	0.073
A3	0.069	0.079	0.069	0.068	0.056	0.094	0.054	0.075
A4	0.059	0.081	0.060	0.058	0.048	0.075	0.044	0.067
A5	0.063	0.073	0.059	0.062	0.049	0.083	0.055	0.072
A6	0.084	0.097	0.087	0.075	0.071	0.111	0.071	0.091
A7	0.062	0.077	0.057	0.055	0.051	0.080	0.059	0.073

---

Results table

This table combines CPUE, traits pr gear type, species density and RaoQ into one results-table.

area	TrapCPUEw	GillnetCPUEw	TrapCPUEn	GillnetCPUEn	TrophicL_Traps	TrophicL_Gillnets	Greg_Traps	Greg_Gillnets	Species_traps	Species_gillnets	RaoQ_All	RaoQ_Traps
1	0.117	1.652	0.060	0.700	3.939	4.075	1.488	2.438	16	14	0.090	0.076
2	0.150	1.176	0.087	1.366	3.914	3.933	1.541	2.295	34	40	0.087	0.066
3	0.136	0.415	0.076	0.455	3.968	4.140	1.556	2.500	23	5	0.063	0.069
4	0.059	0.698	0.067	1.001	3.875	4.262	1.385	1.957	11	23	0.038	0.059
5	0.079	0.904	0.090	1.616	3.831	3.871	1.685	2.200	18	36	0.069	0.063
6	0.135	0.319	0.072	0.975	3.826	4.020	1.602	2.263	24	13	0.063	0.084
7	0.098	0.681	0.075	1.208	3.806	3.716	1.650	1.778	23	38	0.068	0.062